Access Denied: Race, Ethnicity, and the Scientific Enterprise

Access Denied: Race, Ethnicity, and the Scientific Enterprise

George Campbell Jr., Ph.D. Ronni Denes Catherine Morrison

OXFORD UNIVERSITY PRESS

Access Denied

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Access Denied Race, Ethnicity, and the Scientific Enterprise

Edited by

George Campbell Jr., Ph.D. Ronni Denes Catherine Morrison

1 2000

3

Oxford New York Athens Auckland Bangkok Bogotá Buenos Aires Calcutta Cape Town Chennai Dar es Salaam Delhi Florence Hong Kong Istanbul Karachi Kuala Lumpur Madrid Melbourne Mexico City Mumbai Nairobi Paris São Paulo Singapore Taipei Tokyo Toronto Warsaw and associated companies in Berlin Ibadan

Copyright © 2000 by Oxford University Press, Inc. Published by Oxford University Press, Inc. 198 Madison Avenue, New York, New York 10016 Oxford is a registered trademark of Oxford University Press All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of Oxford University Press. Library of Congress Cataloging-in-Publication Data Access denied : race, ethnicity, and the scientific enterprise / edited by George Campbell, Ronni Denes, Catherine Morrison. p. cm. Includes bibliographical references and index. ISBN 0-19-510774-8 1. Science—Study and teaching—United States—Congresses. 2. Mathematics—Study and teaching—United States—Congresses. 3. Engineering—Study and teaching—United States—Congresses. 4. Minorities—Education—United States—Congresses. 5. Minorities in science—United States—Congresses. 6. Minorities in mathematics—United States—Congresses. 7. Minorities in engineering—United States—Congresses. I. Campbell, George, 1945 Dec. 2– II. Denes, Ronni. III. Morrison, Catherine, 1934– . Q181.A1A33 1999 500'.89'00973—dc21 99-10024

1 3 5 7 9 8 6 4 2 Printed in the United States of America on acid-free paper

Preface

n spite of the enormous progress we’ve made since the Civil Rights Era of the 1960s,

IAfrican Americans, Latinos, and American Indians remain significantly under-

represented in science, engineering and mathematics. Less than seven percent of today’s SEM workforce are members of those groups, which constitute 23 percent of the nation’s population. Moreover, the gains achieved during the past quarter of a century are now jeopardized by a dramatic change in the nation’s collective attitude towards efforts to create equity. The NACME Research and Policy Conference on Minorities in Science, Engineering and Mathematics, which gave birth to this book, took place in 1995 as this mood shift began in earnest. The fundamental premise upon which NACME’s mission was constructed and on which our record of achievement rests was being openly challenged. The underpinning concepts of affirmative action and equal opportunity were and still are, regularly under assault, consistently being misinterpreted and misrepresented as preferential treatment or retribution for historical injustices. Our true objective is equitable—not preferential—treatment of minorities. What the nation needs is to develop and utilize the talents of all population groups, to ensure equal access to careers in science, engineering and mathematics fields, to create the opportunity for all to compete in the technological enterprise. However, it must be recognized that a society with deeply rooted racial prejudices demands more than passive and virtually unenforceable equal opportunity laws. If we’re going to succeed in eliminating bias and breaking through the barriers of discrimination—some made of glass, others of concrete—we must have more proactive policies. The objective of the NACME conference was to determine what those policies must be as we approach the new millennium and to translate those policies into action. Like the Sloan-funded study of the underrepresentation of minorities in engineering two decades ago which led to the creation of NACME, the outcome of this meeting has the potential to impact policies that are currently in flux, policies that

vi Preface

go far beyond the science, engineering and mathematics professions, policies that strike at the heart of what I believe has become the critical issue of the post–cold war era: how can we nurture and celebrate the richness of diversity; how can we capitalize on the creative potential inherent in diversity and, at the same time, how do we eliminate the conflict and hostility that can accompany diversity? If we’re going to help shape science and education policies, however, it’s essential to buttress this humanitarian perspective with a strong research foundation. NACME’s historical commitment to research is one of the things that attracted me personally to the organization in 1989. Traditionally, non-profits operate either on the strength of a visionary founder or on the basis of what sounds right or feels good. NACME’s programs have always been driven by the results of sound, well-grounded research. Sometimes that means doing counter-intuitive things that the founders might never have considered. Throughout its existence, NACME has been willing to alter its programs or even to terminate them and shift its strategies in fundamental ways to accommodate more current research findings. Rarely, however, have we had the opportunity to conduct as comprehensive an analysis as we are attempting here. Let me express my deep appreciation to those who made the conference possible. First, a very special thanks to Ted Greenwood and to the Sloan Foundation not only for supporting the meeting financially but for recognizing the importance of this work in general, as demonstrated by the Foundation’s long-term commitment to equitable access to science, engineering and mathematics careers. It was the Sloan Foundation that funded the Planning Commission for Expanding Minority Opportunities in Engineering, which produced the ground-breaking, action-oriented report, Blueprint for Action, in 1974. That report had a profound impact on national policy and provided the impetus for establishing the NACME organization. So I’m grateful to Sloan on several levels. I sincerely thank Eugene Cota-Robles for so ably chairing the meeting. The Advisory and Program Committees, listed on page xi, provided enormously helpful advice and guidance. Of course, the project could not have been so successful without the commitment and dedication of NACME staff. Only those who have been involved in planning an event of the magnitude of this conference can appreciate the level of effort necessary to bring such an undertaking to fruition. Catherine Morrison, who was NACME’s Director of Research at the time of the meeting, was the principal organizer of the conference and is a co-editor of this volume. Ronni Denes, Senior Vice President for Research and Operations, has overall responsibility for NACME research and is the other co-editor. Sangeetha Purushothaman, a member of the research department, and Antoinette Torres, Vice President, Academic Affairs, served as participants, as well as hosts of the meeting. While the practitioners have held a plethora of conferences and meetings on access to science, engineering and mathematics during the past quarter of a century, never before has there been a gathering of so many leading scholars from across the country to focus exclusively on the research and policy agenda in this area. It was quite an honor for NACME, and for me personally, to host such an historic meeting. I’d like to thank all of those who participated in this important venture for conscientiously preparing and studying input papers, for developing thoughtful responses to them and for the hard work that is yet to come as we pursue the agenda that emerged from the conference. G. C. Jr.

Chapter Title

Contents

Conference Committees Introduction 3 Eugene Cota-Robles

xi

I Demographic Framework United States Demographics George Campbell Jr. A Practitioner’s Perspective Carlos Rodriguez

7 42

Policy Issues 43 Alan Fechter

II Early Education Entering the Education Pipeline 49 Shirley Malcom and Bernice Anderson Changing the Face of Science and Engineering: Good Beginnings for the Twenty-First Century Joan Bissell Early Childhood Science Programs Yolanda S. George Obstacles to Policy Formation Antony Ward

84

78

61

vii

viii Contents

III The Middle School Years Influences on Minority Participation in Mathematics, Science, and Engineering 89 Beatriz Chu Clewell and Jomills Henry Braddock II Advancing Middle Grade Reform: Research Anita M. Baker It Takes a Village to Raise a Scientist DeAnna Beane The Policy Perspective Nancy Carson

137

140

143

IV Adolescence Onward The Transition to and from High School of Ethnic Minority Students Angela B. Ginorio and Jeri Grignon Explaining the Unrealized Aspirations of Racial and Ethnic Minorities Barbara Schneider The College Preparation Process Judith Shay

187

Policy Matters 190 R. Guy Vickers

V The Undergraduate Years plus One Barriers to Minority Success in College Science, Mathematics, and Engineering Programs 193 Reginald Wilson The Role of State and Institutional Policies and Practices Richard C. Richardson, Jr. Reflection on the State of Research: What Next? Vincent Tinto

207

212

University Faculty: Priming the Pump or Lying in Ambush? William Yslas Vélez Rethinking the Model Antoinette Torres

219

Financing Opportunity for Postsecondary Education Thomas G. Mortenson

221

215

151 174

Contents ix

VI Early Career Preparation: Academia The Preparation of Minorities for Academic Careers in Science and Engineering: How Well Are We Doing? Shirley Vining Brown Enhancing the Research Base Cheryl B. Leggon

239

269

The Next Stage 273 Cora Marrett Policy Perspectives Daryl E. Chubin

274

VII Early Career Preparation: Industry Models for Studying Early Careers: Minority Scientists and Engineers in Industry 281 Terrence R. Russell Why Are Minority and Women Scientists Still Treated So Badly? Henry Etzkowitz Critical Issues 306 George Campbell Jr. Policy 311 Willie Pearson, Jr. Gaining Access: A Research and Policy Agenda Ronni Denes Index

325

314

295

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Conference Committees

NACME Conference on Minorities in Science, Mathematics and Engineering Advisory Committee Ralph G. Gonzalez National President Mexican American Engineering Society

Howard G. Adams Executive Director National Consortium for Graduate Degrees for Minorities in Engineering (GEM)

Ted Greenwood Program Officer Alfred P. Sloan Foundation

Ramona Brown Director, Office of Recruitment and Retention/PRES City College of the City University of New York (CCNY) (For NAMEPA)

Norbert S. Hill Executive Director American Indian Science & Engineering Society Carol Hollenshead Professor CURIES Coordinating Committee Director, Center for the Education of Women University of Michigan

George Campbell Jr. President NACME, Inc. Patricia L. Chavez Human Resources Management Employee Workforce and Effectiveness U.S. Department of Commerce

Shirley A. Jackson Professor Rutgers University

Theresa Edwards Chair, Mathematics Department Spelman College

Shirley Malcom Director, Education and Human Resource Program American Association for the Advancement of Science

Rey Elizondo Dean, College of Sciences and Engineering University of Texas at San Antonio

xi

xii Conference Committees Catherine Morrison Director, Research NACME, Inc. Willie Pearson Professor Wake Forest University Paula Rayman Professor CURIES Coordinating Committee Center for Research on Women Wellesley College Vincent Tinto Professor Syracuse University

Betty Vetter Executive Director Commission on Professionals in Science and Technology Charles B. Watkins Dean, School of Engineering City College of the City University of New York Luther S. Williams Deputy Director National Science Foundation Reginald Wilson Senior Scholar American Council on Education

NACME Conference on Minorities in Science, Mathematics and Engineering Program Committee Daryl Chubin Director, RED National Science Foundation Beatriz Chu Clewell Senior Research Scientist Educational Testing Service Shirley Malcom Director, Education and Human Resource Program American Association for the Advancement of Science Willie Pearson, Jr. Professor Wake Forest University

Paula Rayman Professor CURIES Coordinating Committee Center for Research on Women Wellesley College Vincent Tinto Professor Syracuse University Reginald Wilson Senior Scholar American Council on Education

Access Denied

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eugene cota-robles

Introduction

lmost ten years ago I met with a number of individuals at the National Academy

A of Sciences to discuss many of the concerns that underlie the call to this NACME

gathering. At that time, I expressed concern for the limited presence of minority scientists and engineers on the faculties of America’s top research universities. In a burst of hyperbole, I maintained that unless we pressed for the appointment of talented African American, Latino, and American Indian scholars to these faculties, by the year 2000 we would face the reality of Academic Apartheid. Today one finds that the proportion of African Americans receiving new research project grants from the National Institutes of Health (NIH) is less than 1 percent. Specific data show that in 1991, only 25 out of 5,217 investigators funded by the National Institutes of Health were African American. Recognizing that the success rate at NIH is approximately 25 percent, one can assume that no more than 100 African American scientists submitted research grant proposals. This indicates that we still have a significant absence of minorities on the faculties of American colleges and universities; thus the extreme view I expressed in 1986 may not have been so extreme after all. The data for the National Science Foundation are not much different: in 1993, minorities (American Indians, African Americans, Hispanics, and Pacific Islanders) received 287 out of the 7,800 research grants awarded. These discouraging figures reinforce the view that, after prodigious efforts to improve the academic pipeline for minorities, we have made little progress in diversifying the science and engineering workforce at its highest levels. What makes the situation even more disturbing is that these efforts have been mounted in a favorable environment. This environment is eroding and may give way to draconian measures, given the recent criticism of affirmative action that has been heard from the courts, the voting public, and political leaders. On October 27, 1994, the Fourth Circuit U.S. Court of Appeals ruled against the University of Maryland’s Banneker 3

4 Introduction

scholarship program, which is merit-based and available only to African American students. As this conference convened, the people of California were being asked to place a so-called California Civil Rights initiative on the 1996 ballot. This measure would bar state agencies, including public colleges and universities, from giving preferences to any individual or group based on race or sex. Given the fact that Proposition 187, the California initiative outlawing services to undocumented immigrants, passed handily in 1994, we should not have been surprised that the Civil Rights initiative known as Proposition 209 passed as easily in 1996. Already the Regents of the University of California are in heated debate about the issue of medical school admissions. They propose to challenge the U.S. Supreme Court’s Bakke decision permitting the use of race as one criterion among those used to select medical school applicants. Interestingly enough, the three minority Regents are leading the charge against Bakke-based admissions, calling them a “crutch” for minorities. In this gloomy environment, we gathered at the conference to “establish the knowledge base regarding the participation of underrepresented minorities of both genders in science, mathematics, and engineering.” The goal of establishing and reviewing such a knowledge base is not only to establish a research agenda for the next decade but also to widely disseminate a set of policy recommendations to increase participation. Performance goals for doctorates have not yet been met. Some people take exception to my emphasis on increasing Ph.D.s. The reason I advocate such an “elitist” goal is because practicing science and engineering faculty control not only college admission and academic offerings but also the ambience in academic departments. They influence the conditions that students must navigate within academic classes, as well as outside of classes and within the discipline as a whole. Faculty are keepers of the entry keys to academic success. We cannot just wring our hands and bemoan this absence of minority faculty. Until the promising efforts at systemic reform of science education and adoption of science standards greatly improve the education of American students, something must be done to increase the flowering of scientific talent among minority students. Thus our presence at the NACME conference was extremely important, and our goal was to take away from here a documented understanding of how the early educational experiences that minority students have influence their later academic successes. In these challenging times, it will no longer be possible for us to use our intuition. We must recognize that without understanding, we will repeatedly be told that our lack of participation is impossible to solve. Out of this conference will come an in-depth analysis of available research to document such understanding and a set of recommendations based on our discussion of the findings. With this agenda in hand, it is incumbent on us to foster the success of minority students under the most demanding academic conditions at the nation’s leading institutions. We need more honors students who will eventually change not only the face of the faculty but also the way faculty assume responsibility to insure diversity, throughout our institutions of higher education, in the truest sense of the word.

i

DEMOGRAPHIC FRAMEWORK

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george campbell jr.

United States Demographics

n 1876, Edward A. Bouchet received his Ph.D. in physics from Yale University.

IJust two years earlier he had graduated Summa Cum Laude and Phi Beta Kappa,

also from Yale, with a bachelor’s degree in physics. Trained as an experimentalist, the young physicist’s research interests were in the propagation and transmission of electromagnetic radiation. Bouchet’s remarkable academic achievements, extraordinary training and early scientific contributions while still a student suggested a promising future in physics. Except for one thing. Edward Bouchet was black, and the door to the scientific research community in America was not yet open to African Americans. Bouchet’s doctorate was the first ever awarded to a black student by an American university in any field. Bouchet was also most likely the first African American to earn membership in Phi Beta Kappa. Initially intent on a research career after his graduation, Bouchet could find a job only at the Institute for Colored Youth in Philadelphia, where he taught chemistry and physics. He went from there to a teaching position at a high school, to a hospital administrative post, to a job as customs inspector, to an administrative position at a normal school. Finally, in his last job, he served as principal of a high school in Ohio. What few records remain unequivocally establish him as an inspiring teacher with unparalleled dedication. But, in spite of his historic academic breakthrough, Bouchet has been virtually lost to history. None of his own writings or papers have survived. Even his burial place is unknown. 1 The tragic story of Edward Bouchet’s life is not a surprise to anyone familiar with American history. Racial attitudes in the late nineteenth century scientific community were cogently described by Robert Bruce in his 1987 book, The Launching of Modern Science 1846–1876: Even if a black had somehow acquired a sound scientific education, race prejudice—not only among Southerners but also Northerners such as Henry, Bache,

7

8 Demographic Framework Peirce, and Agassiz—would surely have blocked him or her from a career or even a hearing in science.2

It is noteworthy, however, that physics was the first profession to allow penetration of the racial barrier, at least with respect to education at the highest degree level. It was during the same time period, the dawn of the twentieth century, that fundamental scientific breakthroughs created an authentic paradigm shift, a virtual revolution in the field of physics. Relativity and quantum theory dissolved the rigid principles and deterministic perspective of classical physics. Scientists in this emerging era of enlightenment were perceived to be steeped in the pursuit of objective truth. It would, therefore, have been easy at that time to anticipate a leadership role for science in breaking down the irrational, obsessive American practice of racial exclusion. But it was not to be. In fact, a half century later, the Nobel Prize in physics for discovery of the transistor legitimized the decidedly unscientific voice of William Shockley, as he used the platform inadvertently erected by the prize to promote his thinly veiled theory of white supremacy. From the day that Bouchet received his Ph.D., almost a hundred years would go by before the door to the American scientific enterprise would begin to creak open perceptibly to people of color, though there have been a few notable exceptions. We shall show later that members of three ethnic groups constituting 28.5 percent of the college-age population in the United States—African Americans, Latinos, and American Indians—remain significantly underrepresented and underutilized in all of the natural science, engineering, and mathematics disciplines, without exception. In physics and astronomy, Bouchet’s field, among the 1,652 doctorates awarded nationwide in 1995, only 10 went to African Americans, 23 to Latinos, and 2 to American Indians, adding up to scarcely 2 percent of the total. Narrowing the focus to women of color, the story is even more alarming. The first African American woman to receive a Ph.D. in physics was Shirley Ann Jackson. The year was 1974! After her doctorate from Massachusetts Institute of Technology, however, she did go on to a distinguished career as a theoretical physicist at the nation’s premiere industrial research facility, then known as Bell Telephone Laboratories.3 From there she went on to serve as professor of physics at nearby Rutgers University, and in 1996 she was appointed chair of the Nuclear Regulatory Commission by President William Clinton. But in the years 1975 through 1995, only 15 other African American women, 40 Latinas, and 3 American Indian women would join her among the ranks of physicists.

Underrepresentation Defined We consider a population group underrepresented if the members of a profession include a significantly smaller proportion of people from that population group than exists in the total working-age population. Similarly, underrepresentation among the degree recipients in a given year exists when graduates do not reflect the college-age population distribution. This does not mean to suggest that the ethnic distribution in science, engineering and mathematics (SEM) should exactly mirror the ethnicity of our population. However, where there are extraordinarily large gaps, as there are

United States Demographics 9

in SEM—professions that are absolutely crucial to the nation’s security, social stability and economic well-being—it’s critically important to understand the obstacles limiting access to careers in those fields, to devise strategies to eliminate the impediments and to analyze the accompanying national policy implications. To that end, we first examine the current and projected ethnic makeup of the American population. The U.S. demographic profile of the twenty-first century will differ significantly from that of the twentieth. Immigration from Latin American and Asian countries will continue to provide a large influx of first-generation Americans. Some time near the middle of the next millennium, the proportion of non-Hispanic whites, now almost three-quarters of the population, will drop below 50 percent. Those who belong to groups that are now referred to as “minorities” will collectively constitute a majority of the American population.4 That is, no individual ethnic or racial group will comprise a majority. Already, as we approach the year 2000, African Americans, Latinos, and American Indians—those groups underrepresented throughout the American economic mainstream—produce one-third of the births in this country and make up 30.8 percent of the kindergarten through twelfth-grade school enrollment (figures 1 and 2). Already these groups add up to 30.1 percent of those under eighteen years old, 28.5 percent of the college-age cohort (18 to 24 years old), and 23.3 percent of the total population.5 Projections to the year 2020 show that these proportions will grow to 38.9 percent for those under 18 years old, 35.8 percent for the college-age cohort, and 30 percent of the total population (figures 3–5). The overall labor force profile, and particularly the SEM work force, is also in the midst of significant change with respect to gender. Women already constitute more than 42 percent of the full-time labor force, 56 percent of the undergraduate

figure 1 Share of Births, 1996. Data source: National Science Foundation, Women, Minorities and Persons with Disabilities in Science and Engineering: 1994, NSF 94-333, Arlington, VA, 1994, pre-publication data presented at the NACME Research and Policy Conference on Minorities in Science, Engineering and Mathematics by Mary J. Golladay, NSF program director

10 Demographic Framework

figure 2 School Enrollment, Elementary and Secondary, 1994. Data source: National Science Foundation, Women, Minorities and Persons with Disabilities in Science and Engineering: 1994, NSF 94-333, Arlington, VA, 1994, pre-publication data presented at the NACME Research and Policy Conference on Minorities in Science, Engineering and Mathematics by Mary J. Golladay, NSF program director

enrollment, and 54 percent of the graduate school enrollment in the United States. 6 In several SEM disciplines, women are approaching parity in the number of both bachelor’s and doctoral degrees they receive annually and are rapidly closing the work force gap. In other SEM fields, specifically in physics and engineering, however, women continue to lag far behind. Asian Americans, by our definition, are not underrepresented in SEM. This group constitutes 3.4 percent of the total population and is expected to reach 5.8 percent by 2020. By 1993, however, Asian Americans comprised 13.5 percent of the SEM doctorates and 11.9 percent of the overall SEM labor force. Nevertheless, they do

figure 3 Under 18 Population, 1996 v. 2020. Data source: The Official Guide to Racial and Ethnic Diversity, Cheryl Russell, New Strategist Publications, New York, 1996

United States Demographics

11

figure 4 College-Age Population, 1996 v. 2020. Data source: The Official Guide to Racial and Ethnic Diversity, Cheryl Russell, New Strategist Publications, New York, 1996

face some of the obstacles that are endemic to small minority populations in a large society with well-established cultural values and disquieting racial attitudes. Examining the demographic profiles more closely, some analysts have suggested that AsianAmerican SEM professionals are dominated by recent immigrants and that those whose families immigrated to America several generations ago have academic, economic, and work force profiles that are closer to those of underrepresented minority group members.7 This is an area where further research could be illuminating, particularly since this hypothesis represents a significant deviation from the typical pattern for immigrant populations. There is some evidence, for example, that the opposite pattern prevails, even among immigrant Mexican Americans.8

figure 5 Total Population Distribution, 1996 v. 2020. Data source: The Official Guide to Racial and Ethnic Diversity, Cheryl Russell, New Strategist Publications, New York, 1996

12 Demographic Framework

The demographics, together with a profile of the six major SEM professions that we focus on throughout this discussion—physics and astronomy (combined), chemistry, the biological sciences, the mathematical sciences, computer science, and engineering—delineate the severity of the underrepresentation problem for African Americans, Latinos and American Indians. The collective proportion of these groups in the United States who held doctorates in 1995 ranges from a dismal 2.4 percent in physics and astronomy to an almost equally meager 4.6 percent in computer science (figure 6). Considering all degree levels, in 1993 the fraction of minorities in the labor force ranged from 2.8 percent in physics and astronomy to 10.5 percent in the mathematical sciences, and fall between 5 and 7 percent in the other areas (figure 7).

Physical Sciences Though physics was the first scientific profession to admit an African American to the highest degree, today it has the adverse distinction of being among the most exclusive of the natural scientific fields. From 1975 through 1995, American universities awarded 26,202 doctorates in physics and astronomy. During that period, the number of African Americans receiving doctorates in the field fluctuated more or less randomly between five and ten, with no discernible growth trend. Similarly, the number of American Indians receiving Ph.D.s bounced between zero and five. Only Latinos exhibited a growth trend, with doctorates numbering in the twenties in recent years. However, much of the growth in Latino doctorates can be accounted for by the Latino population expansion. Overall, among the 26,202 doctorates awarded from 1975 to 1995, only 170 (0.65 percent) went to African Americans, 321 (1.23 percent) went to Latinos (U.S. citizens and permanent residents), and 33 (0.13 percent) went to American Indians (figure 8).9 About a third went to resident and non-

figure 6 Minority PhDs in the U.S., SEM, 1995. Data source: Commission on Professionals in Science and Technology, Professional Women and Minorities: A Total Human Resource Data Compendium, 12th ed., Washington, DC, 1997

United States Demographics

13

figure 7 Minorities in the Labor Force, SEM, All Degrees, 1993. Data source: Commission on Professionals in Science and Technology, Professional Women and Minorities: A Total Human Resource Data Compendium, 12th ed., Washington, DC, 1997

resident foreign students. Excluding foreign nonresidents, the 524 doctorates that went to underrepresented minorities still amounted to only 2.8 percent of the remaining total. Underrepresented minority women during this time period received only 68 doctorates, representing only 0.22 percent of the total and one-eighth the number awarded to underrepresented minority men. The pattern of doctoral awards in chemistry during the same two decades is very similar to what we saw in physics. However, the absolute number of chemistry doc-

figure 8 Minority PhDs in Physics and Astronomy, 1975–1995. Data source: Commission on Professionals in Science and Technology, Professional Women and Minorities: A Total Human Resource Data Compendium, 12th ed., Washington, DC, 1997

14 Demographic Framework

torates awarded to minorities reflects the fact that chemistry is a larger profession that typically produces almost 50 percent more doctorates than physics in any given year. The number of African American doctorates fluctuated between ten and just over 30 and the number of American Indian doctorates ranged between zero and ten, with neither showing sustained progress. The number of Latino doctorates, exhibiting some growth, has climbed above 40 in recent years, with a sharp but, apparently, transient peak of 59 in 1994. Among the 38,381 doctorates awarded in chemistry between 1975 and 1995, 468 (1.22 percent) went to African Americans, 624 (1.63 percent) went to Latinos (U.S. citizens and permanent residents), and 70 (0.18 percent) went to American Indians (figure 9) for a total of only 3 percent. 10 Almost a quarter went to foreign students. Excluding foreign nonresidents, the 1,162 doctorates that went to underrepresented minorities amounted to only 3.9 percent of the remaining total. Underrepresented minority women received 295 doctorates, only 0.77 percent of the total. However, as meager as the number is for minority women, it is more than a third of the number for minority men, a much larger fraction than we saw in physics. At the bachelor’s degree level, most published data do not include disaggregated figures for the physical sciences listed by ethnicity and gender, although some of the professional societies, such as the American Institute of Physics, collect data separately for the individual fields they represent. This is quite unfortunate because the profile of physics is very different from that of chemistry and other physical science disciplines, particularly with respect to gender. For all ethnicities, the proportion of physics graduates who are women is much lower than that of chemistry graduates. The overall proportion of minority graduates in physics is also lower than that of chemistry.

figure 9 Minority PhDs in Chemistry, 1975–1995. Data source: Commission on Professionals in Science and Technology, Professional Women and Minorities: A Total Human Resource Data Compendium, 12th ed., Washington, DC, 1997

United States Demographics

15

figure 10 Minority Physical Science Degrees, BS, 1979–1994 (% of total). Data source: National Science Foundation, Women, Minorities and Persons with Disabilities in Science and Engineering: 1996, NSF 96311, Arlington, VA, 1996

As shown in figure 10, from 1979 through 1994, unlike what we saw at the doctoral level, there was a very definite upward trend in B.S. degrees awarded to underrepresented minorities in the physical sciences. 11 The overall percentage of minorities among B.S. degree recipients in 1994, 9.8 percent in the physical sciences, is also considerably higher than that for doctoral recipients in either physics and astronomy (2.6 percent in 1994) or chemistry (4.3 percent). However, given that these population groups constituted more than 28 percent of the college-age population in 1994, under 10 percent of the B.S. degrees is still far from parity. A very disturbing circumstance that shows up in the gender disaggregation for the physical sciences is that B.S. degrees for African American men declined substantially from 1981 through 1990, though there has been a slight recovery since then (figure 11). Another interesting feature of the bachelor’s degree data is that among minority recipients, women receive a significantly larger proportion than is the case for nonminorities. The most dramatic case is that, in recent years, African American women have typically received more B.S. degrees in the physical sciences than African American men (figure 12). Labor force statistics, particularly for those at the doctoral level, should generally reflect degree data for the past two or three decades unless there are biases in the transition from graduate training to the job market. Since minority degree production in physics, astronomy, and chemistry has changed very little over the past two decades, we should expect the percentage of minorities in the physical sciences labor force to mirror the average percentage of doctoral degrees, excluding nonresident foreign graduates. This is indeed the case. Underrepresented minorities comprise about 2.4 percent of doctoral physicists and astronomers and 3.6 percent of the chemists (figure 6),12 fractions which are very close to their 2.8 percentage standing among doctorates in physics and astronomy and 3.9 percent among doctorates in chemistry

16 Demographic Framework

figure 11 Minority Physical Science Degrees, BS, 1979–1994 (N). Data source: National Science Foundation, Women, Minorities and Persons with Disabilities in Science and Engineering: 1996, NSF 96-311, Arlington, VA, 1996

awarded to U.S. citizens and permanent residents since 1975. From published data, it appears that minority physical scientists at the doctoral level have a slightly higher inclination to pursue careers in academia than other sectors, since they comprise more than 5 percent of the higher education faculty in this area. However, one of the research challenges is that different data sources—in this case the Bureau of the Census at the Department of Commerce, versus the National Center for Education Statistics of the Department of Education—reflect different survey methodologies, and we cannot draw significant conclusions from such small differences.

figure 12 Women Physical Science Degrees, BS, 1979–1994. Data source: National Science Foundation, Women, Minorities and Persons with Disabilities in Science and Engineering: 1996, NSF 96-311, Arlington, VA, 1996

United States Demographics

17

Biological Sciences From 1975 through 1995, universities in the United States awarded 86,184 doctorates in the biological sciences, of which 69,617 or 80.8 percent, went to American citizens and permanent residents. As with the physical sciences, the most notable attribute of the time series is the lack of substantial progress in the number of minority doctorates. Smoothing out the occasional peaks and valleys, the annual number of African Americans who earned doctorates hovers at about 50 and the annual number of American Indians is typically about 10. Again, only the Latino population shows a significant growth trend, exceeding 100 in recent years (figure 13). Overall, among the 86,184 doctorates awarded during the 21 years, only 1,096 went to African Americans (1.27 percent), 1,264 (1.47 percent) went to Latinos (U.S. citizens and permanent residents), and 168 (0.19 percent) went to American Indians.13 Excluding foreign nonresidents, the 2,528 doctorates awarded to underrepresented minorities amounted to only 3.6 percent of the remaining total. Underrepresented minority women received 1,079 doctorates: 1.3 percent of the total, but almost three-quarters of the number awarded to underrepresented minority men. This is a very different proportion compared to that for the physical sciences. In fact women received a higher proportion of doctorates in the biological sciences than in the physical sciences among all ethnic groups, but the difference is more pronounced for underrepresented minority populations. At the bachelor’s level, the proportion of degrees in the biological sciences awarded to minorities has remained flat since the mid-1980s. Nearly 6 percent has gone to African Americans and another 6 percent to Latinos, while American Indians have been getting about 0.5 percent (figure 14).14 For all population groups except African Americans, about half of the bachelor’s degrees in the biological sciences

figure 13 Minority PhDs in Biological Sciences, 1975–1995. Data source: Commission on Professionals in Science and Technology, Professional Women and Minorities: A Total Human Resource Data Compendium, 12th ed., Wasington, DC, 1997

18 Demographic Framework

figure 14 Minority Biological Science Degrees, BS, 1979–1994 (% of total). Data source: National Science Foundation, Women, Minorities and Persons with Disabilities in Science and Engineering: 1996, NSF 96311, Arlington, VA, 1996

have gone to women. Among African Americans, women received nearly 70 percent (figures 15 and 16). Contributing to this high percentage of women is the same disturbing trend that appeared in the physical sciences: the number of African American men receiving B.S. degrees in the field declined precipitously—by almost 25 percent—between 1979 and 1994. In the labor force, underrepresented minorities comprised 4.0 percent of the doctoral level biological scientists in 1995 and 3.5 percent of the doctoral life sciences faculty (a broader category that includes the agricultural sciences) in 1993.

figure 15 Minority Biological Science Degrees, BS, 1979–1994 (N). Data source: National Science Foundation, Women, Minorities and Persons with Disabilities in Science and Engineering: 1996, NSF 96-311, Arlington, VA, 1996

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figure 16 Women Biological Science Degrees, BS, 1979–1994. Data source: National Science Foundation, Women, Minorities and Persons with Disabilities in Science and Engineering: 1996, NSF 96-311, Arlington, VA, 1996

Taking into account all degree levels, underrepresented minorities made up 6.8 percent of the 1993 labor force in the biological sciences. This is a somewhat higher proportion than we found with the physical sciences and tracks well with the higher proportion of degrees in the field.

Mathematical Sciences Mathematics, along with physics, ranks among the least inclusive of the SEM disciplines at the doctoral level and produces very few degrees from the minority community in absolute numbers. From 1975 through 1995, American universities awarded 18,850 doctorates in mathematical sciences, with 40 percent going to foreign students. Among those, African Americans received 163 (0.86 percent), Latinos (U.S. citizens and permanent residents) received 201 (1.07 percent), and American Indians received 21 (0.11 percent). Filtering out the nonresident foreign graduates, underrepresented minorities received 3.3 percent of the remaining doctorates. As in the case of the national sciences, only the Latino population exhibited a growth trend, and even in the case of Latinos, the numbers are so small that the growth could hardly be called a trend (figure 17). The maximum number of doctorates in mathematics awarded to Latinos during this period was only 16. Proportionally, minority women fared much better in mathematics than in physics; however, again, the numbers are so small that comparing percentages is not particularly illuminating. Between 1975 and 1995, 32 African American women received doctorates in mathematics, as did 46 Latinas and 4 American Indian women.15 Bachelor’s degrees awarded to underrepresented minorities in mathematics is on par with other SEM fields, a distinct departure from the doctoral results. This can be attributed, in part, to the greater demand for mathematics graduates with just

20 Demographic Framework

figure 17 Minority PhDs in Mathematics, 1975–1995. Data source: Commission on Professionals in Science and Technology, Professional Women and Minorities: A Total Human Resource Data Compendium, 12th ed., Washington, DC, 1997

a bachelor’s degree as compared to, say, physics or chemistry B.S. graduates. While the proportion of minorities was stagnant throughout the 1980s at between 7 and 8 percent, there has been a marked growth trend since 1990, reaching 11.5 percent of the total in 1994 (figure 18).16 As with the biological sciences, women of all ethnic groups are close to parity with men within their own group in the number of B.S. degrees. The lowest ratio occurs for Asian American women who, in 1994, received 44 percent of the degrees in the Asian American population group. African American women have the highest ratio, obtaining 54 percent of degrees awarded to African Americans in 1994 (figures 19 and 20).

figure 18 Minority Mathematical Science Degrees, BS, 1979–1994 (% of total). Data source: National Science Foundation, Women, Minorities and Persons with Disabilities in Science and Engineering: 1996, NSF 96-311, Arlington, VA, 1996

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figure 19 Minority Mathematical Science Degrees, BS, 1979–1994 (N). Data source: National Science Foundation, Women, Minorities and Persons with Disabilities in Science and Engineering: 1996, NSF 96-311, Arlington, VA, 1996

The 1995 labor force distribution shows that underrepresented minorities made up 4 percent of the mathematical sciences doctorates. Data disaggregated by ethnicity for faculty positions in mathematics are not readily available; however, minorities comprised 5.2 percent of the combined doctoral faculty in mathematics and computer science for all types of higher education institutions in 1993. (An independent source with its own survey methodology, the Computing Research Association, reports that in 1995–1996 underrepresented minorities comprised 1.7 percent of the computer science faculties in higher education.) Considering all degree levels,

figure 20 Women Mathematical Science Degress, BS, 1979–1994. Data source: National Science Foundation, Women, Minorities and Persons with Disabilities in Science and Engineering: 1996, NSF 96-311, Arlington, VA, 1996

22 Demographic Framework

underrepresented minorities constitute a higher proportion of the mathematics labor force, at 10.5 percent, than all of the other SEM fields. But, again significant inferences cannot be made, because overall labor force statistics are given in thousands, and rounding the data for minorities can introduce significant errors.

Computer Science Computer science is unique among the SEM disciplines considered here in that it is a new field in which few doctorates were awarded before the mid-1970s. It’s not surprising, therefore, that, in absolute numbers of degrees awarded to minorities, there was a very definite growth trend from 1975 through 1995 (figure 21).17 However, the numbers remain quite small. Only once did African Americans receive as many as 11 doctorates in one year. The maximum for Latinos was 16 and for American Indians, 3. Among the 8,634 Ph.D.s awarded in computer science during the 21 years, African Americans received 57 (0.66 percent), Latinos received 100 (1.16 percent), and American Indians received 14 (0.16 percent). In the aggregate, underrepresented minorities received 3 percent of the 5,695 doctorates awarded to American citizens and permanent residents. Of the 171 minority doctorates, 38, or 22.2 percent, went to women: 21 to African American women, 14 to Latinas, and 3 to American Indian women. Another distinguishing attribute of the field is that a bachelor’s degree permits direct entry into the research and development community. This is reflected in the relatively large number of bachelor’s degrees that minorities obtain in computer science that do not flow into advanced degrees. In 1994, African Americans received almost 11 percent of the total B.S. degrees, Latinos 5 percent, and American Indians 0.4 percent. Among all SEM fields, computer science produces the largest fraction

figure 21 Minority PhDs in Computer Science, 1975–1995. Data source: Commission on Professionals in Science and Technology, Professional Women and Minorities: A Total Human Resource Data Compendium, 12th ed., Washington, DC, 1997

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figure 22 Minority Computer Science Degrees, BS, 1979–1994 (% of total). Data source: Commission on Professionals in Science and Technology, Professional Women and Minorities: A Total Human Resource Data Compendium, 12th ed., Washington, DC, 1997

of underrepresented minority bachelor’s degrees, reaching an aggregate of 16.3 percent in 1994 (figure 22).18 African American women receive more B.S. degrees in the discipline than African American men. For other groups, women received from one-fifth to a little more than one-third of the total degrees awarded to members of their respective groups (figures 23 and 24). Although computer science is such a young field and produces few minority Ph.D.s annually, labor force statistics show a relatively large fraction of underrepresented minorities even at the doctoral level. This suggests that minorities who do get their

figure 23 Minority Computer Science Degrees, BS, 1979–1994 (N). Data source: Commission on Professionals in Science and Technology, Professional Women and Minorities: A Total Human Resource Data Compendium, 12th ed., Washington, DC, 1997

24 Demographic Framework

figure 24 Women Computer Science Degrees, BS, 1979–1994. Data source: Commission on Professionals in Science and Technology, Professional Women and Minorities: A Total Human Resource Data Compendium, 12th ed., Washington, DC, 1997

degrees persist in the field as professionals. The 4.6 percent underrepresented minority doctorates in the labor force tops all other SEM fields (figure 6). Considering all degree levels, underrepresented minorities constitute 6.6 percent of the work force in computer science (figure 7).

Engineering Engineering has the distinction of being the largest of the SEM disciplines, employing about two million professionals (figure 25), as compared to just over a half million for all of the natural sciences and 1.3 million for mathematics and computer science. Engineering is, therefore, larger than all of the natural sciences, mathematics and computer science combined. The demand for engineers in the United States has grown constantly throughout this century, almost independent of the state of the economy, and a steady supply of highly skilled, well-trained engineers has long been recognized as the lifeblood of wealth creation and economic development. Corporate leadership is today heavily dominated by engineers. Going back to the 1970s, more than half of the chief executive officers of Fortune 500 companies had an engineering background. African Americans, Latinos, and American Indians—a smaller fraction of the population at that time than they are today, but growing rapidly— were virtually invisible in engineering, comprising only 1 percent of the work force. The promise of rapidly shifting demographics, coupled with this severe underrepresentation problem and the ever-growing corporate demand for engineers, led to a unique, highly organized effort to increase access for minorities and gave birth to a non-profit corporation, the National Action Council for Minorities in Engineering, Inc., to lead that effort. While a number of programs to increase minority participation in the other SEM fields—developed by the National Science Foundation,

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figure 25 SEM Labor Force, All Degrees, 1990. Data source: Commission on Professionals in Science and Technology, Professional Women and Minorities: A Total Human Resource Data Compendium, 12th ed., Washington, DC, 1997

the professional societies and other organizations—have emerged over the years, none of them has the benefit of a sustained effort over a quarter of a century. While underrepresentation in engineering persists, substantial progress has been made. The number of doctorates awarded to minorities has more than tripled, from 42 in 1975 to 142 in 1995 (figure 26).19 Only the biological sciences produced more in 1995 (207 doctorates, up from 81 in 1975). Nevertheless, engineering still lags other SEM disciplines in the proportion of doctorates that go to minorities. The 142 awarded in 1995 was just over 2 percent of the total engineering Ph.D. awards in that year, and only 29 of the 142 went to women. Of the 86,115 doctorates granted from 1975 through 1995, only 588 (0.68 percent) went to African Americans, 756 (0.88 percent) went to Latinos, and 67 (0.08 percent) went to American Indians.

figure 26 Minority PhDs in Engineering, 1975–1995. Data source: NACME

26 Demographic Framework

Like computer science, engineering does not required a Ph.D. to enter the profession as a member of the R&D community. Moreover, jobs for bachelor’s degree recipients have traditionally been plentiful. Even during the early 1990s, when the cold war ended and the defense industry—the largest employer of engineers—was dismantled and when the largest technology-intensive corporations went through massive downsizing, the demand for engineers still dwarfed that for people trained in any other area. As a consequence, engineers typically command the highest starting salaries of any discipline. These factors place additional pressures on engineering graduates and particularly minorities, who are more likely to come from poor families, to enter the work force immediately after the first degree. Between 1975 and 1995, bachelor’s degrees obtained by minorities more than quadrupled, growing from 1,463 to 5,931, outpacing all other SEM disciplines in terms of absolute numbers (figure 27)20 ; however, the proportion continues to lag behind all other fields except physics (figure 28). The 5,931 minority graduates in 1995 still comprised only 9.2 percent of the total. Engineering also has the most severe underrepresentation problem for women in all ethnic groups. Even at the bachelor’s level, in any given year through 1995, women overall have never received more than 18 percent of the degrees. However, the proportion of women in engineering varies for different ethnic groups (figures 29 and 30). African American women in 1995 received 35.2 percent of the degrees awarded to African Americans. Women from all other ethnic groups were close to the overall ratio for women, i.e., close to 20 percent. This is distinct among SEM disciplines. In most other fields, women are approaching parity in the number of B.S. degrees compared to men of the same ethnic group. In 1993, underrepresented minorities constituted just 3.3 percent of the doctoral engineers in the labor force, and, combining all degree levels, only 5.9 percent of the total engineering work force (figures 6 and 7).

figure 27 Minority Engineering Degrees, BS, 1975–1996. (N). Data source: NACME

United States Demographics

figure 28 Minority Engineering Degrees, BS, 1975–1996. (% of total). Data source: NACME

figure 29 Minority Engineering Degrees, BS, 1991–1996. Data source: NACME

figure 30 Women Engineering Degrees, BS, 1991–1996. Data source: NACME

27

28 Demographic Framework

Bachelor’s to Graduate Degree Conversion A doctorate is virtually a requirement for professional status in the physical, biological, and mathematical sciences fields. In engineering and computer science, the master’s degree can be considered the first professional degree; however, a doctorate is, of course, essential for an academic career path. In analyzing the underrepresentation problem, it is, therefore, useful to examine the critical transition from bachelor’s degree to graduate school and to look at the proportion of first-degree recipients who go on to advanced degrees in each discipline, by ethnicity and gender. Specifically, the number of Ph.D.s relative to the average number of B.S. degrees five to seven years earlier for a given population group offers a crude measure for comparative purposes of persistence to the doctorate. Similarly, we use the number of master’s degrees relative to the number of bachelor’s degrees two years earlier to measure persistence to professional status in engineering and computer science. Figure 31 shows, as we would expect, that the conversion of bachelor’s to doctoral degrees is highest for the natural sciences. Mathematics is somewhat unique in that a large number of jobs are available in precollege teaching, which requires only a bachelor’s degree. We’ve already noted that engineering and computer science graduates can enter the R&D enterprise and earn relatively large salaries with only a bachelor’s degree, so it’s not surprising that those fields have the lowest conversion rates. The pattern for bachelor’s conversions to master’s degrees for engineering and computer science is similar to the pattern for conversion to doctorates for the other disciplines (figures 32 and 33). Comparing the conversion rates by ethnicity and gender shown in the figures yields results that are at least moderately surprising. The rates for minority and nonminority men in the physical sciences and mathematics are relatively close. In the biological sciences, however, the gap is quite significant. In all fields, minority women are least likely to pursue advanced degrees and, in most cases, by a significant amount.

figure 31 1995 SEM Doctorates, Percent of Average 1988–1990 BS Degrees. Data source: NACME

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figure 32 1995 Engineering Master’s Degrees, Percent of 1993 BSE Degrees. Data source: NACME

Precollege Demographic Patterns Underrepresented minority children constitute more than 30 percent of the total enrollment of all American schools, and 54 percent in central cities, as defined by the Census Bureau. By the year 2020, African American, Latino, and American Indian children will make up 38.9 percent of the under-eighteen population (figure 3). Providing high-quality education for young people from all ethnic groups, and more effectively preparing them for our future work force, must become a national priority. We have invested billions of dollars annually in education reform in recent years. However, based on a variety of student performance measurements and benchmarks, as reported in the National Assessment of Education Progress, 21 the Third

figure 33 1993 Computer Science Master’s Degrees, Percent of 1991 BS Degrees. Data source: NACME

30 Demographic Framework

figure 34 TIMSS Results, 12th Grade Mathematics. Data source: U.S. Department of Education, National Center for Education Statistics, Pursuing Excellence: A Study of U.S. Twelfth-Grade Mathematics and Science Achievement in International Context, NCES 98-049, U.S. Government Printing Office, Washington, DC, 1998

International Mathematics and Science Study,22 or the Goals 2000 Report,23 very little progress has been achieved for American students from all ethnic groups (figures 34–37). And though the gaps between minorities and nonminorities in mathematics and science achievement have narrowed slightly, they still loom very large (figure 38). At the root of the problem is an enduring belief that there is a widely varying innate and immutable intelligence among children. Belying cognitive research outcomes, the American education establishment remains unwilling to internalize the idea that all children can learn—especially mathematics and science—at high levels, that academic achievement is more a function of effective effort than genetics. In addition, because selection criteria include a great deal of subjectivity, the pervasiveness of tracking systems continues to disproportionately assign minority students to lower-level classes (figure 39). Low expectations of students not in the highest tracks deny opportunity to the vast majority of nonminority students as well. No amount of spending, curriculum reform, national testing, or learning technology will solve our education problem as long as teachers believe that the majority of students can’t learn mathematics and science. During the industrial age, a fraction of the population with a high-quality mathematics and science education sufficed to meet the work force needs, but in today’s world that will not serve either the work force or the national interest well. Only 15 percent of American students take the academic mathematics and science curriculum required to major in an SEM field. For minority students, it’s 6 percent (figure 40).24 In contrast, calculus and physics, which are not even available at many American high schools, are required courses in many of our competitor nations.

United States Demographics

figure 35 TIMSS Results, 12th Grade Science. Data source: U.S. Department of Education, National Center for Education Statistics, Pursuing Excellence: A Study of U.S. Twelfth-Grade Mathematics and Science Achievement in International Context, NCES 98-049, U.S. Government Printing Office, Washington, DC, 1998

figure 36 National Education Goals Panel, Math Achievement v. Standards, 1996. Data source: National Education Goals Report, Building a Nation of Learners, 1997, National Education Goals Panel, U.S. Government Printing Office, Washington, DC, 1997

31

32 Demographic Framework

figure 37 National Education Goals Panel, Science Achievement v. Standards, 1996. Data source: National Education Goals Report, Building a Nation of Learners, 1997, National Education Goals Panel, U.S. Government Printing Office, Washington, DC, 1997

figure 38 NAEP Average Proficiency Scores, Age 17, 1973–1994. Data source: U.S. Department of Education, National Center for Education Statistics, National Assessment of Education Progress, Trends in Academic Progress: Achievement of U.S. Students in Science, 1969 to 1994: Mathematics, 1973 to 1994, U.S. Government Printing Office, Washington, DC, 1996

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figure 39 Tracking by Ethnicity. Data source: National Center for Education Statistics, “Education Life,” New York Times, January 10, 1993

Role of Immigration To meet our SEM work force needs, the United States has come to rely heavily on foreign-born scientists and engineers. This is particularly true of engineering, the largest of the disciplines. In 1995, more than 65,000 engineers immigrated to this country. That is equal to the number of engineers American universities produced that year at the bachelor’s level. Almost half of the engineering doctorates awarded by American universities in recent years have gone to foreign students. In 1995,

figure 40 High School Course Completion. Data source: U.S. Department of Education, National Center for Education Statistics, The Condition of Education 1996, NCES 96-304, U.S. Government Printing Office, Washington, DC, 1996

34 Demographic Framework

figure 41 Foreign-Born Doctoral SEM Faculty, 1993. Data source: Commission on Professionals in Science and Technology, Professional Women and Minorities: A Total Human Resource Data Compendium, 12th ed., Washington, DC, 1997

foreign-born students comprised more than 40 percent of the engineering graduate school enrollment, and immigrants held more than 60 percent of the postdoctoral R&D positions in the field. Forty percent of doctoral engineers resident in the United States were born elsewhere, as were 30 percent of the engineering faculty at American universities. In contrast, underrepresented minorities constitute 3.3 percent of the doctoral engineers in the labor force, and, combining all degree levels, only 5.9 percent of the total engineering work force. The other SEM fields are not far behind in the utilization of foreign-born talent (figures 41–45). 25

figure 42 U.S. Foreign-Born Resident PhDs, 1993. Data source: Commission on Professionals in Science and Technology, Professional Women and Minorities: A Total Human Resource Data Compendium, 12th ed., Washington, DC, 1997

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figure 43 Foreign Post-Doctorates, 1995. Data source: Commission on Professionals in Science and Technology, Professional Women and Minorities: A Total Human Resource Data Compendium, 12th ed., Washington, DC, 1997

The point here is not to discourage immigration of talented students and professionals. However, it is essential for us to understand the impact on the evolving employment market and the potential implications of our growing dependence on immigrants while neglecting the educational development of significant components of the American student population. In the oil crisis of 1973, we saw what can happen when we depend too heavily on foreign sources for critical commodities, and in today’s world, human capital is our most valuable resource. Recent changes in the increasingly global labor market suggest that the United States is, in many ways,

figure 44 Foreign SEM Doctorates, 1995. Data source: Commission on Professionals in Science and Technology, Professional Women and Minorities: A Total Human Resource Data Compendium, 12th ed., Washington, DC, 1997

36 Demographic Framework

figure 45 Foreign Graduate School Enrollment, SEM, 1995. Data source: Commission on Professionals in Science and Technology, Professional Women and Minorities: A Total Human Resource Data Compendium, 12th ed., Washington, DC, 1997

becoming more vulnerable and less favorably positioned to attract well-trained scientific and engineering talent. Competitor nations are expanding their investments in research and development relative to the GNP and constructing state-of-the-art R&D facilities rivaling the best that America has to offer. Along with the globalization of SEM labor markets also comes greater parity in earning potential across national boundaries. Some foreign governments are establishing financial incentives for top expatriate scientists and engineers to return to their homeland. Foreign students earning their degrees in the United States are increasingly inclined to return to their native lands to practice their professions (figure 46).26 While equalization of labor costs reduces the pressures on U.S. companies to export R&D jobs, if we do not produce the necessary work force to meet the demand, American companies will be forced not only to export more R&D jobs but often to pay higher labor costs. Clearly, the continuing over-reliance on foreign engineers is risky. Fortunately, it isn’t necessary. There is an enormous reservoir of talent within both the underrepresented and the majority populations of America which can and must be developed.

Minority Groups: Similarities and Differences The three groups that are substantially underrepresented today—African Americans, Latinos, and American Indians—have had a very different history in this country. Latinos constitute a heterogeneous group—Mexican Americans, Puerto Ricans, Dominicans, other Caribbean immigrants, and Central and South American immigrants—with a multiplicity of cultures, historical backgrounds, and experiences in America, bound together by a more or less common linguistic heritage. Although the data unequivocally indicate that the collective community of Latinos remains underrepresented in science, engineering, and mathematics, some scholars have

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figure 46 Foreign Engineering Graduates (PhD), Stay Rates. Data source: The Official Guide to Racial and Ethnic Diversity, and U.S. Council of Economic Advisors, Economic Report of the President, U.S. Government Printing Office, Washington, DC, 1998

argued that the progress of Latinos very closely tracks that of other American immigrant groups. Linda Chavez suggests that economic growth, improved education, and advancement into the middle class are masked by rapidly increasing immigration rates of the poor.27 A corroborating study, conducted by the Rand Corporation back in 1986, and, therefore, somewhat dated, showed a rapid rate of education assimilation among Mexican Americans with each succeeding generation.28 This raises what might be an interesting current research question: what is the degree of underrepresentation of science, engineering and mathematics among nonimmigrant Latinos relative to immigrant Latinos? Linda Chavez further argues that Latinos would be far better off eschewing American ethnic and racial politics to pursue a strategy of assimilation, following the pattern of Italian-Americans, Greek-Americans, IrishAmericans, and other immigrant groups of the earlier part of the twentieth century. A complicating factor is that Latinos also include a multiplicity of ethnicities and races. What is often ignored, in fact, is that there are powerful cultural and genealogical linkages among all of the underrepresented minority groups. Many Latino populations have very strong genealogical ties both to West Africa and to the indigenous American Indian communities. So racial discrimination, along with powerful economic factors and language barriers, has historically played a role in impeding the progress of, at least, some segments of the Latino population. In addition, in many parts of the country, intermarriage of African Americans and American Indians was not uncommon in the eighteenth and nineteenth centuries. Moreover, although formal marriages between whites and blacks or whites and Indians were relatively rare, informal child-bearing relationships were not. In general, the ambiguities associated with race and ethnicity constitute a stand-alone sociological research topic that could have significant bearing on the development of effective public policies aimed at creating a more equitable society.

38 Demographic Framework

The overall history of American Indians since the arrival of European settlers is characterized, in the words of Russell Thornton, as a long holocaust, defined by forced migration, destruction of a way of life, a demographic collapse brought about by warfare, genocide, and the introduction of diseases imported from Europe. 29 The indigenous Indian population declined from what is estimated at about six million in the fifteenth century to barely 250,000 at the turn of the twentieth century. The current population stands at two million, where much of the recovery occurred during the past two decades and is associated with a recent change by the Bureau of Census in the definition of American Indians. According to Thornton, with the increasing Indian population in the last half of the century, there has also been growing urbanization, stimulated in part by the United States Bureau of Indian Affairs relocation program that began in 1950. Urbanization has in turn given rise to lower rates of tribal affiliation, a dramatic rise in intermarriage and increased rates of assimilation. Between 1970 and 1980, the number of Indians married to non-Indians increased from 33 percent to 50 percent. This raises a research question similar to one that emerged in considering the Latino population: what is the degree of underrepresentation in science, engineering and mathematics separately among assimilated and unassimilated Indians, urban and rural Indians, Indians residing on reservations or tribal trusts and rural Indians living elsewhere? The social, economic, and political infrastructure of America, throughout its history, has been spinning a complex web interconnecting and, at the same time, constraining the African American, Latino, and American Indian populations. In response, at times the groups have joined forces in a concerted effort to break free of the web’s restraints. Other times, the web became a battlefield, pitting the groups one against the other in an ill-fated political strategy to gain an advantage. Depending upon political expediency, the groups have competed for the right to be considered, on the one hand, the most victimized and, on the other hand, the most powerful, most influential minority population. In any case, today all three groups remain substantially underrepresented in the economic mainstream and specifically in the science and engineering professions, though the origins, causes, and mechanisms that continue to inhibit progress may be somewhat distinctive. An important area for research resides in developing an understanding of these differences and formulating strategies and policies targeted more to the specific problems faced by each group. Whatever the outcomes, it is important to understand that, politically, it’s rare, if ever, an advantage to be perceived as weak, downtrodden, and helpless victims of oppression, and competition for the status of social nadir is destructive for winners and losers alike.

Research Issues Principal outcome goals of the NACME Research and Policy Conference and of this book are to identify key research issues, to determine where the gaps are in our knowledge base and to define a research agenda for the future that can, ultimately, help resolve the issues and close the gaps. In this chapter, the primary sources of data are:

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NACME, Inc. The Engineering Workforce Commission The National Science Foundation The National Center for Education Statistics, U.S. Department of Education The Bureau of the Census, U.S. Department of Commerce The Bureau of Labor Statistics, U.S. Department of Labor Commission on Professionals in Science and Technology Science and engineering professional societies

The most significant research issue with respect to demographics is the lack of consistent national survey standards, including definitions of terms, organization or aggregation of data, and survey methodology. Even among the various government agencies, where the bulk of the demographics data are collected, there is a lack of consistency. For example, some sources, in counting engineers, include leaders or managers of technology-intensive businesses who have engineering backgrounds while others include only those directly involved in engineering work or who have the label “engineering” in their job title. Published degree tables typically show disaggregated physical sciences data at the doctoral level but not at the bachelor’s level, and the list of specific subdisciplines included in physical sciences is not the same in all databases. Mathematics and computer science are often, but not always, aggregated. Gender and ethnicity cannot be disaggregated in some databases. Those population groups comprised of a multiplicity of ethnicities are sometimes double-counted. Latinos, for example, can be of any race, and some databases include them under the label “Hispanic,” as well as in their respective racial categories. Non-U.S. citizens are also aggregated in different databases. Some have data disaggregated by citizenship status and others by residence status, that is, permanent residents are often but not always combined with U.S. citizens. Inconsistencies in the collection and reporting of data cause different kinds of problems. In general, they limit the precision with which demographic analyses can be conducted. In some cases, using different data sources can lead to significantly different conclusions. Mapping one data set into another can be very difficult, for example, in tracking the conversion of bachelor’s degrees to doctorates, when aggregation of data is different at the different degree levels.

Conclusions The overarching conclusion that emerges from the evolving American demographics, work force statistics, and degree production data is that African Americans, Latinos, and American Indians remain drastically underrepresented and underutilized in the science, engineering, and mathematics professions, professions that play a crucial role in national wealth creation and economic growth. In spite of significant efforts by the National Science Foundation, other government agencies, a number of universities, community-based organizations, and the corporate sector, progress has been startlingly slow, and in many cases virtually nonexistent.

40 Demographic Framework

While the legacy of prejudice in the United States is certainly a factor in the degree of underrepresentation that exists, the history of racial and ethnic discrimination is not the focus of this book. Our premise is that, whatever the source or origins, the huge gaps in participation in our technology-based economy, with the potential for irreversibly isolating particular ethnic groups, has profound implications for the standard of living of all Americans and can, ultimately, impoverish us all. Although the current political climate is openly hostile to programs directed at improving opportunities for minority groups, it is evident that without a more effective, sustained effort to expand access to SEM fields than we’ve had in the past, we risk losing more generations of talent and, given the demographics, American leadership in technology and competitiveness in the global economy. Countervailing efforts that have been gaining ground in the 1990s—efforts to maintain the status quo, to preserve the elite status of advanced, high-quality education, to deny equitable educational resources to disadvantaged communities—will not serve the nation well. We cannot afford to continue our drift towards the classic third-world social order of haves and have nots, where individual communities are separated by steel gates, stone walls, independent schools, private security forces, and insurmountable economic gaps. Already young people from families in the top 25 percent of income in the United States are 13 more times likely to earn a college degree by the age of 24 than those from the bottom quartile. 30 And, what is even more ominous, the gap is widening. When we compound this growing bifurcation of economic classes by ethnic differences, that is, when each class is predominantly inhabited by members of a distinctive ethnic group (figure 47),31 the history of the world up to the present tells us that we have an inherently unstable social order. If we further compound class and ethnic isolation with a gap in access to the power of technology or to career opportunities in technical fields, we will have a profoundly

figure 47 The Economic Gap. Data source: National Science Board, Science and Engineering Indicators, 1996, Arlington, VA, 1996

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volatile situation. What kind of a society will we become if we don’t take action today to prevent the emergence of a permanent underclass, ethnically isolated and estranged by limited access to technology? Our hope is that those who feel threatened and who want to maintain the status quo of SEM as an exclusive, elite work force and to “protect” the SEM corner of the job market against diversity will come to recognize that developing and utilizing the minority talent pool will not be at the expense of others, that strengthening our human resources is not a zero sum game, that embracing the value of diversity and the natural creativity it engenders will enrich the technical work force and expand opportunities for all, and that there will be no losers.

Notes 1. R. E. Mickens. 1989. “Bouchet and Imes: First Black Physicists,” in A. M. Johnson, ed., Proceedings of the 12th Annual Meeting and 16th Day of Scientific Lectures of the National Society of Black Physicists, pp. 1–14, National Society of Black Physicists. 2. R. V. Bruce. 1987. The Launching of Modern American Science 1846–1876. New York: Alfred Knopf, p. 78; quoted by Mickens. 3. In 1984, upon divestiture of AT&T and the Bell System, Bell Telephone Laboratories became AT&T Bell Laboratories. Upon further divestiture of AT&T in 1996, Bell Laboratories became part of Lucent Technologies. 4. National Science Foundation. 1994. Women, Minorities and Persons with Disabilities in Science and Engineering: 1994, NSF 94-333. Arlington, VA: NSF. Pre-publication data presented at the NACME Research and Policy Conference on Minorities in Science, Engineering and Mathematics by Mary J. Golladay, NSF Program Director. 5. C. Russell. 1996. The Official Guide to Racial and Ethnic Diversity. New York: New Strategist Publications. 6. U.S. Department of Education, National Center for Education Statistics. 1996. Digest of Education Statistics, 1996, NCES 96-133. Washington, DC: U.S. Government Printing Office. 7. Private Communication, Betty Vetter, 1993. 8. Business-Higher Education Forum. 1990. Three Realities: Minority Life in the United States, p. 25. Washington, DC: Business-Higher Education Forum. 9. Commission on Professionals in Science and Technology. 1997. Professional Women and Minorities: A Total Human Resource Data Compendium, 12th ed. Washington, DC: U.S. Government Printing Office. 10. Ibid. 11. National Science Foundation. 1996. Women, Minorities and Persons with Disabilities in Science and Engineering: 1996, NSF 96-311. Arlington, VA: NSF. 12. Commission on Professionals in Science and Technology. 13. Ibid. 14. National Science Foundation. 1996. 15. Commission on Professionals in Science and Technology. 16. National Science Foundation. 1996. 17. Commission on Professionals in Science and Technology. 18. National Science Foundation. 1996. 19. Engineering Workforce Commission. 1995. Engineering and Technology Degrees, 1975–1995. Washington, DC: U.S. Government Printing Office.

42 Demographic Framework 20. National Action Council for Minorities in Engineering, Inc. 1996. Leading Change, NACME Annual Report. 21. U.S. Department of Education, National Center for Education Statistics. 1996. Trends in Academic Progress: Achievement of U.S. Students in Science, 1969 to 1994: Mathematics, 1973 to 1994. Washington, DC: U.S. Government Printing Office. 22. U.S. Department of Education, National Center for Education Statistics. 1998. Pursuing Excellence: A Study of U.S. Twelfth-Grade Mathematics and Science Achievement in International Context, NCES 98-049. Washington, DC: U.S. Government Printing Office. 23. National Education Goals Panel. 1997. Building a Nation of Learners, 1997. Washington, DC: U.S. Government Printing Office. 24. U.S. Department of Education, National Center for Education Statistics. 1996. The Condition of Education 1996, NCES 96-304. Washington, DC: U.S. Government Printing Office. 25. Commission on Professionals in Science and Technology. 26. Russell. See also, U.S. Council of Economic Advisors. 1998. Economic Report of the President. Washington, DC: U.S. Government Printing Office. 27. L. Chavez. 1991. Out of the Barrio: Toward a New Politics of Hispanic Assimilation. Basic Books. 28. J. P. Smith and Finis R. Welch. 1986. Closing the Gap: Forty Years of Economic Progress for Blacks. Santa Monica, CA: The Rand Corporation. 29. R. Thornton. 1987. American Indian Holocaust and Survival: A Population History Since 1942. Norman, OK: University of Oklahoma Press. 30. T. G. Mortenson. 1995. Financing Opportunity for Postsecondary Education. NACME Research and Policy Conference on Minorities in Science, Engineering and Mathematics. 31. National Science Board. 1996. Science and Engineering Indicators, 1996. Arlington, VA.

carlos rodriguez

A Practitioner’s Perspective In the world of educational research, it is a constant challenge to balance the triangulated scale of practice, policy, and research. The work and substance of contemporary education usually cannot be deconstructed or segmented easily among these three components. Within practice, policy, and research symbiotic relationships and couplings exist. Ideally we would wish that these relationships and couplings would be readily explicit and discernible so that they could inform each other in a spiraling, evolutionary manner: information building policy leading to informed policy building

United States Demographics Policy Issues 43

practice leading to effective practice fueling the questions for more insightful information, and so on. However, in the real world we often find ourselves struggling with decoupled and decontextualized information. We often ask big questions and then throw a lot of information at the questions, hoping some of it will stick. Setting the stage for a meeting of the minds through a demographic framework is a mean task, especially since most of us have been grappling with the knowledge of the exigencies of the numbers. Because no matter how you look at the participation of women and people of color in science, engineering and mathematics (SEM), the numbers are simply not there to suggest that our journey toward parity is proceeding faster than a snail’s pace. The most popular framework relies on economic indicators. Accordingly we believe that we have lost or are losing or slowly regaining our position as an economic superpower; that the “anticipated” labor shortages in SEM have reached crisis status; and that shoring up the SEM pool of native-born applicants would bolster U.S. economic competitiveness. But are these valid assumptions? What proportion of GNP/GDP is attributable to SEM? What proportion of our import and export markets are driven by SEM-related activities? These are relevant questions not usually considered by the SEM equity or parity argument beyond the issue of labor shortages. We must establish the necessity of educating and training women and minorities in SEM for the real economic benefits of SEM production. This could provide incentives to funders as well as motivate students and Congress. What is the cost of underrepresentation? What does it cost the individual who may have the talent and interest to pursue science, engineering, or math but doesn’t; what is the societal cost? Can we measure the societal value of minority and women scientists and doctors who serve the needs of minority communities in ways that non-minorities have not, and the significant costs their loss would bring? In answer we can point to the impact minority faculty and teachers have had on minority student success and completion rates. Individual and social rates of return are as important and immediately more compelling than the standard outcome deficiency data. And they would prove that it costs us more money to maintain underrepresentation.

alan fechter

Policy Issues Of the two types of policy issues, one is associated with the macro environment and the other with the micro environment. Within the macro environment, we face severely constrained budgets for some time to come. Public concern about budget

44 Demographic Framework

deficits at both the federal and state/local levels will make it extremely difficult to consider ambitious new programs. In addition, there is growing concern that we may be overproducing new Ph.D.s. Strong evidence of a glut has been provided by the professional societies, especially in the mathematical and physical sciences and in engineering. Given these two factors—sharply constrained government spending and perceived overproduction of Ph.D.s—why should we invest scarce budgetary resources in programs that have as their objective increasing the supply of Ph.D.s.? Today’s realities mean that targeted efforts to reduce underrepresentation will be viewed as a zero-sum game, with white males as the losers. The political implications of this perception cannot be ignored. Given the need for such efforts, the micro environmental issue would be to determine the most effective combination of programs/activities to ameliorate underrepresentation. To accomplish this would require information about (1) the existence of underrepresentation (which groups and what magnitude), (2) the reasons for its existence, and (3) the effectiveness/efficiency of programs/activities aimed at reducing underrepresentation. I strongly believe there is a need for programmatic efforts to increase the representativeness of our science/engineering (S/E) work force. This belief is based on two arguments. First, because we are not now drawing deeply from the pools of underrepresented groups (i.e., women and underrepresented racial/ethnic minorities), I believe that, at the margin, the quality of candidates for careers in S/E is higher than that of white males. Thus, we can increase the quality of the S/E work force by changing its gender and racial/ethnic composition (i.e., by increasing the representation of women and underrepresented minorities) even if the size of this work force remains the same. Second, I believe that members of underrepresented groups bring to this work force a unique perspective and set of interests that influence the choice of research and problem areas to be addressed. By addressing these areas, the contributions of S/E to the welfare of our society are enhanced above and beyond what they otherwise would have been. Given the need for activities to increase the diversity of the S/E work force, our challenge is to determine how the initial three questions can be illuminated by existing or new data collection and analysis.

Strengths and Limitations of Available Data I will here confine myself to sources provided by federal agencies. In so doing, I recognize that I exclude from consideration the substantial amount of information collected and/or compiled by private organizations (such as the ACE, the Engineering Workforce Commission, and the Commission on Professionals in Science and Technology), by professional societies (such as the AMS, the ACS, and the APS), and by individual researchers (such as NACME and Seymour and Hewitt). The major federal sources of data include the following: • Division of Science Resources Studies (SRS) of the National Science Foundation (NSF), which supports the production of data on doctorates, scientists and engineers at all degree levels, recent S/E graduates at the

United States Demographics Policy Issues 45

baccalaureate and master’s degree levels, S/E degrees at each degree level, and on graduate and postdoctoral students; • Bureau of Labor Statistics (BLS), which produces data on employed scientists and engineers; and • National Center for Educational Statistics (NCES) of the Department of Education, which supports the collection of degree data, longitudinal data on cohorts of high school students, panel data on recent college graduates, and data on the achievements of precollege students. The most important databases for the purposes of formulating policy to alleviate underrepresentation are those of the SRS and the NCES. The SRS data are useful for analyses at the undergraduate and graduate levels and in the workplace. The NCES longitudinal surveys of cohorts of high school students are rich data sources for examining behavior at the precollege (K–12) level. The BLS data cannot be used to derive meaningful estimates of employment of racial/ethnic minorities in science or engineering occupations. Moreover, because of sample-size limitations, I believe that even the SRS and NCES data on minorities can’t be used to produce detailed tabulations of more than two dimensions for scientists and engineers. Underrepresentation of racial/ethnic minorities has been well documented using these data sources. The data clearly show that underrepresentation increases as you move up through the educational system. The factors associated with underrepresentation are also well understood, but we have thus far been unable to determine with any precision the relative contribution of each factor—in particular, the unique contribution of socioeconomic factors, on the one hand, and gender/race/ ethnicity factors, on the other. It seems intuitively obvious that the mix and relative importance of different factors will vary among racial/ethnic groups and perhaps between generations or socioeconomic classes within a given racial/ethnic group. Longitudinal data can be helpful in documenting the flows into and out of S/E (in terms of such indicators as student interest, career plans, etc.) and can help in determining where in the educational system the net losses are concentrated or where in postgraduation careers attrition is greatest. But flow data, while necessary, do not provide sufficient information for formulating policy. One also needs to know what motivates the flows. This requires an understanding of behavior (e.g., career choice, mobility, etc.). While the factors associated with such behavior are generally well known, their relative importance is not well understood. Analyses of large-scale databases have the potential for illuminating this issue by identifying statistical associations. But these associations reflect more fundamental underlying behavioral patterns that may be manipulated through policy measures. These patterns may be better understood through nonquantitative research techniques (e.g., ethnographic studies or focus groups). The challenge, however, would be to translate the findings from such research into effective policy recommendations, given the idiosyncratic (i.e., nonrepresentative) nature of the subjects of such studies. Questions concerning the effectiveness of current programs are difficult to answer based on existing data. Although there is an extensive literature on such programs, the conclusions one can draw from it are quite limited because it is either (a) anecdotal, idiosyncratic and/or unrepresentative, or (b) flawed by inappropriate

46 Demographic Framework

methodologies (e.g., lacking proper controls or reflecting a selectivity bias in the choice of the experimental group). Despite these limitations, the information to be gleaned from this literature can yield some meaningful insights, provided the limitations inherent in the information is clearly recognized and taken into account. The current situation of fiscal stringency is creating mounting pressure for accountability. Such accountability will require the institution of processes that will assure that proper evaluations of programs are made. The NSF is working with the Education and Human Resources Committee of the National Science and Technology Council to achieve this objective. In instituting such a system, it will be important to recognize that the system must be capable of producing both “hard” and “soft” information. We face a significant challenge, in the current environment of perceived abundance of human resources and real fiscal constraint, to defend targeted programs to increase the diversity of the S/E work force. To confront this challenge, we need to identify the reasons underrepresentation continues to exist, and assess the effectiveness and costs of efforts to alleviate it. Underrepresentation has been well documented, and the possible reasons for such underrepresentation are also well understood, but the relative importance of each is less clear and will require further statistical and ethnographic study. We least understand the effectiveness and costs of efforts aimed at alleviating underrepresentation. It is important that this gap be filled through both quantitative and qualitative approaches.

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EARLY EDUCATION

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shirley malcom and bernice anderson

Entering the Education Pipeline

The End and Beginning of the Pipeline In 1993, Ph.D.s in the natural sciences were awarded by U.S. universities to 149 African Americans, 251 Hispanics, and 19 American Indians, representing increases of 66 percent, 170 percent, and 36 percent, respectively, over 1983 figures. In the same year, doctorates in engineering were awarded to 50 African Americans, 65 Hispanics, and 2 American Indians, increases of 72 percent, 124 percent, and 100 percent, respectively, over 1983 figures (National Science Foundation 1993). These numbers reflect changes at the end of the education pipeline for American minorities. Understanding these results requires that we look much earlier in the lives of these scientists and engineers, at the education climate they faced as they moved through the system. The majority of those who earned science and engineering (S/E) doctorates in the early 1990s were born in the late 1950s or early 1960s, received their precollege education in the 1970s, and completed college in the 1980s. Many probably benefitted from intervention programs developed to increase participation of minorities in science, engineering, and biomedical fields. But a retrospective analysis of the investment in such interventions has led many to conclude that the impacts have been marginal compared with the dollars spent (Science 1993). Others steadfastly defend these program investments. Strong arguments can be made that the size and timing of the investments affected the overall impact of the support. For example, much of the early funding of intervention programs was directed at the collegiate level (later shifting to the high school level), ignoring the large losses from the talent pool of minority students that had already occurred (Malcom, Cownie, and Brown 1976). In 1975, the American Association for the Advancement of Science (AAAS) undertook a study of intervention programs aimed at increasing American Indian, 49

50 Early Education

African American, and Hispanic student interest and participation in science, mathematics, engineering, and biomedical education and careers between 1960 and 1975. Some 355 programs and projects were identified; few of these were aimed at students below the high school level. Writing at that time, Malcom noted: More than 45 percent of the programs undertaken to improve the science education of minority students were aimed exclusively at the undergraduate level, while only 7 percent and 18 percent were specifically targeted at the elementary and high school levels, respectively. These data become immediately significant if one notes that most minority scientists and educators identify precollege as the level at which the greatest need exists for additional coordinated and sustained effort to increase the pool from which minority scientists come. (1976)

The study recognized that the sample of programs might be biased in favor of well-funded, nationally connected, and university-based programming, and that even university-based projects that depended on a continuous flow of students lacked comprehensiveness. The problems were particularly severe for those racial/ethnic groups that had high secondary school dropout rates and that lacked a network of higher education institutions around which interventions could be organized. The decade between 1978 and 1988 saw the emergence of more comprehensive programs (for example, Resource Centers for Science and Engineering supported by the National Science Foundation) and an attempt to reach younger students (such as Sloan support for precollege minorities in engineering initiatives). While investment in intervention programs may have begun by emphasizing recruitment of students to science, mathematics, engineering and technical (SMET) fields, funders were slower to recognize retention and completion of such programs as a deliberate strategy for increasing the numbers of students. “Readiness for study,” providing the set of K–12 experiences (and as argued in this chapter, pre-K as well) that permits students to successfully matriculate in college-level science and mathematics courses, was not generally understood as pivotal to an overall strategy of federal support until the late 1970s and early 1980s. Despite more than thirty-five years of social science research stressing the impact of family and community influences on overall educational achievement, most of the attention of intervention efforts emphasized reform of schools and schooling; this included programs that “replaced” the functions of schooling for a small number of students with strong interest in science and engineering education and careers. The fragmented nature of these efforts, along with unfunded, underfunded, or inconsistently funded programs, makes it difficult to declare that the experiments undertaken since 1970 to bring underparticipating groups to science and engineering have failed. Over time the most successful strategies have been retained in programs with balanced focus given to inschool, out-of-school, community, and parental influences (Malcom et al. 1984). But this is the end of the story: the making of the 1993 class of doctoral scientists and engineers and the history of the times in which they were educated. This focus on the Ph.D.s has been the dominant part of the story as program developers struggled to put in place programs, policies, and projects to address the fact that the science and engineering community did not (and still does not) resemble America, especially its future population. Arguments focused on the doctoral level because the statistics

Entering the Education Pipeline 51

of underparticipation were more timely and disaggregated for this group; however, the numbers tell us little about the experiences of those who completed their doctoral education, and they reveal nothing about those who aspired but failed to complete the process. We are even hard pressed to explain the ups and downs of the Ph.D. production for different groups over time. It is clear that those who seek to encourage participation by minorities in SMET through policy avenues have decided to look to the other end of the “numbers line,” when the talent pool is still filled with all kinds of people.

Policies to Promote Diversity The Interim Report of the Task Force on Women, Minorities, and the Handicapped in Science and Technology, released in 1988, was among the earliest policy documents to address the issue of underparticipation by minorities in SMET, as well as the need to examine the experiences of children before they enter formal schooling. The Task Force was established by Congress through Public Law 99-383, Section 8, to report to the president and to the heads of the fifteen participating agencies, on a long-term plan to broaden participation in science and engineering. The Interim Report proposed six national goals, addressing policy commitments to educate all children, pre-K to 12 education, higher education, leveraging of federal R&D funding, federal employment, and cultural influences, especially the power of the media to influence popular attitudes toward science and engineering. Goal 2 (pre-K to 12) defined the need to examine the early experiences of children for those policy makers and analysts interested in the makeup of the future population and work force and the needs of the nation as a whole for science: A child born today will be in the sixth grade in the year 2000. That child will graduate from high school in 2006, from college in 2010, and enter the workforce when our society is about equally divided between young and old. These children will live in a world in which science and engineering will be crucial to the workplace and the economy, the conduct of public affairs, and the way we lead our private lives. Ignorance of mathematics and science—or fear of it—will hinder these children as workers and citizens. Because children start to become engineers, scientists, or science-literate early in life, we should provide preschool programs that lay the groundwork for academic skills. This is especially important for the 14 million children who live in poverty, one-third of whom are from minority groups. (1988:17)

Of course this was not the first recognition of the need and value of early experiences. But massive early intervention programs, such as Head Start, did not carry such explicit ties to school content as was being suggested in this document. The National Education Goals that emerged from the Charlottesville Education Summit convened by President Bush in 1989 were adopted by the nation’s governors in 1990 and ratified by Congress in 1994 as the centerpiece of the Goals 2000: Educate America Act (Public Law 103-227). Again, the tie between preschool experiences and later school learning was made explicit in the articulation of Goal 1: By the year 2000 all children will enter school ready to learn.

52 Early Education

In January 1990, the Quality Education for Minorities (QEM) Project issued a report, Education That Works: An Action Plan for the Education of Minorities, on the readiness goal for minority children and proposed indicators for determining when it would be achieved: “This goal will be achieved when we: increase access to quality pre- and post-natal care; increase participation in child nutrition programs; ensure that every pre-schooler has access to quality day care and early childhood education; enable all parents to better assume their roles as first teachers of their children.” With its support for quality health care before and after birth, recognition of the critical role of parents as first teachers, and support for quality early care and education and for out-of-school experiences, the QEM report recognized the need to develop a “substrate for learning” to support any specific focus of SMET education.

The Health Connection Starting Points, a report issued in 1994 by the Carnegie Task Force on Meeting the Needs of Young Children, made headlines across the country. While this effort was largely a synthesis of previous research, the report’s conclusions shook the nation’s perceptions of the lives of its youngest citizens. Starting Points cited the work of the National Education Goals Panel, which in its 1993 report noted that nearly half of all infants and toddlers in the United States confronted one or more major risk factors, including inadequate prenatal care, isolated parents, substandard child care, poverty, and insufficient stimulation. Many of these factors are interrelated and may be associated with the age and educational status of the parents, especially the mother. In its report, Healthy People 2000, the U.S. Department of Health and Human Services noted that whereas 76 percent of all pregnant women in the United States receive prenatal care during the first trimester of pregnancy, this is true for only 60–61 percent of American Indian, African American, and Hispanic women (1990). African Americans, American Indians, and Puerto Ricans have infant mortality rates substantially higher than the national average and African American women are reported to be twice as likely as white women to deliver prematurely, have babies with low birth weight, and experience infant and fetal death. The report cites several factors that may contribute to these findings, including young maternal age, less education, and inadequate prenatal care. Risk continues into early childhood as minority populations, especially African Americans and Hispanics, have lower immunization levels by age two than the rest of the population. The effects of poverty and low educational attainment interact to threaten the healthy development of minority children, which in turn may affect the behaviors of the adults around them and the physiological environment for the children’s early brain development. A number of the findings reported in Starting Points relate to the substrate for learning: • Brain development that takes place before age one is more rapid and extensive than previously realized. Brain cell formation is complete before birth but brain maturation (linking up of cells one to another through

Entering the Education Pipeline 53

•

• •

•

synaptic connections) proceeds at an astounding rate after birth, leading to biochemical patterns of astonishing qualitative resemblance between the brains of a one-year-old and those of a normal young adult. Brain development is more vulnerable to environmental influence than was ever suspected. This includes not only the nutritional support for growing a healthy brain, but also the effects of early experiences and stimulation on brain structure and function. Enriched early environments have short- and long-term benefits. The influence of early environment on brain development is long lasting and the effects may be cumulative over time. The environment affects not only the number of brain cells and number of connections among them, but also the way these connections are “wired.” Sensory experiences, especially in the early years, determine brain architecture. Evidence exists for the negative impact of early stress on brain function. The social environment (love or no love, nurturing or no nurturing, etc.) can create a hormonal environment which adversely affects brain function, including learning and memory (Carnegie Task Force 1994).

The brain and the body develop through the complex interactions of the genes and the environment. Both brain and body respond to experiences, positive and negative.

It Matters Who Your Parents Are Beyond the basic hereditary material that they receive from their parents, the environment in which children grow and develop is critically important for their future success. Fourth-grade students’ performance in science on the National Assessment of Educational Progress (NAEP) is closely related to the educational level of the parents; the scores of students whose parents attended and graduated from college are higher than those of students whose parents did not complete high school. They are also closely related to where the students live and attend school; students from disadvantaged urban schools perform less well than those from advantaged urban or rural schools. Clearly these factors are interrelated and influenced by family resources (financial and intellectual) and aspirations, and the kind of learning environment created for the student. These same influences inform a similar pattern of achievement on the NAEP mathematics assessment: African American, American Indian, and Hispanic students perform far below the levels of Asian/Pacific Island and white students (Mullis, Dossey, Owen, and Phillips 1991). The confounding of various factors that are correlated with lower achievement makes it difficult to determine what effects have been measured and what any of this means: income/wealth; education of parents; community in which students live; race/ ethnicity; language spoken in the home; and schooling experiences and resources. Those who develop interventions and advance policies to address gaps in achievement levels cannot choose to pursue options that ignore family and community en-

54 Early Education

vironment; nor can they pursue strategies that depend on altering them. It is essential to determine the experiences, behaviors, resources, and social networks that accompany success and support achievement, and to seek to promote them.

The World of Children Schools The single most important educational resource available to a school-age child should be his or her school. Around 94 percent of five- and six-year-olds of all races are enrolled in school, including private and public kindergarten programs. What a student can learn depends on the learning opportunities available and on what is taught. A survey of various activities undertaken as a part of the kindergarten program revealed that 49 percent of classes use science and math objects each day; fortunately only 18 percent reported using worksheets in these subjects. But the science/math focus was clearly subordinate to the emphasis on language development, play, creative arts, and motor activities. Interestingly, daily use of science/math objects was more common among teachers who had majored in early childhood education (53 percent) as opposed to those who had not (45 percent). Learning opportunities depend on many factors, including the quality of the curriculum and instructional staff; the availability and use of appropriate materials; facilities; the quality of the school program; expectations of teachers, parents, and students; and the attitudes of parents, peers, and community toward study and achievement (Oakes 1990; National Education Goals Panel 1993). Oakes’ work suggests that poor and minority students are less likely to have the kind of educational inputs associated with high-level performance on tests of science and mathematics achievement. She also reports that the discrepancies in inputs are much greater in the secondary years than in the elementary years. Little science is provided to any students during the elementary years. Oakes notes variances in instructional strategies; minority schools provide about half the amount of hands-on and laboratory experiences as schools with predominantly white student populations. While it is easy to decry the gaps that remain today in fourth-grade achievement levels in science and mathematics, it is important to note the tremendous gains that have been made over the years. Between 1977 and 1992, African American students showed an increase of over 18 percentage points among students achieving at the basic level (a knowledge of everyday science facts)—from 72.4 percent in 1977 to 90.7 percent in 1992. Hispanic students showed a similar level of improvement for the basic level—from 84.6 percent in 1977 to 92.4 percent in 1992. While the gap was virtually closed at the basic proficiency level (science knowledge), a large gap remains at the next proficiency level: understanding simple scientific principles. African American fourth-grade students increased proficiency, from 27.2 percent achieving at this next level in 1977 to 51.3 percent in 1992. Hispanic students gained, from 42 percent in 1977 to 55.5 percent in 1992. This is compared with 78 percent of all nine-year-olds achieving at this proficiency level in 1992. Less progress was seen for the growth in the “application” and “analysis” proficiency levels for African Ameri-

Entering the Education Pipeline 55

can and Hispanic students over this same time period. The good news is that we know that we can effect a change in achievement levels. The bad news is that the kind of instruction needed to effect the right kind of change—higher thinking skills— has not yet been realized for most of these students. Home The single most important educational input for children before entering school is the home. Even after entering school, research confirms the importance of the home as a learning environment (U.S. Department of Education 1994). Most of the research has examined the development of social skills, language acquisition, and literacy. Less is known about the specific home contributions that influence science and mathematics learning. Existing data on home factors are not disaggregated by race and certainly not by sex within racial/ethnic groups. Not only does the family serve in a direct instructional mode; it also mediates and facilitates access to other learning environments and experiences. This is especially the case for children in the early years, who are more dependent and tend to be more closely supervised. For this age group, virtually every decision—foods provided, housing, type of care, toys, availability of learning materials, visits to community resources—is made or mediated by parents/primary care givers. Strong Families, Strong Schools (U.S. Department of Education 1994) identifies several factors that affect parents’ involvement with schools and their children’s education. These include available time, uncertainty about what to do, cultural barriers, and lack of a supportive environment. There has been a rise in the number of two-wage-earner families, one-parent families, and parents holding more than one job. Clearly these changes in American families and in the economic realities they face affect the time available for parenting. Parents who were not themselves successful in school and teen parents who have not yet completed school may be at a loss to know what to do. Parents from different cultures, who may not speak English, or even native born parents may feel alienated by the school culture. Most programs for the professional preparation of teachers include little formal training in strategies for working with parents and involving them in school programs or in homebased support of learning. In addition, lack of encouragement by the school or other community institutions, as well as unsupportive employers, may contribute to weak parental involvement. If schools offer no flexibility in their schedule of activities or appointments, and if employers do not allow workers time off for this activity, even the most enthusiastic parents may find parental involvement in school activities to be difficult. The resources made available to children are tied to such factors as family income, the parents’ education, and the presence of two parents. These factors in turn affect access to health care, community resources, provision of in-home education resources, interaction with schools, and so on. Even when resources are free, there may be differential use. For example, African Americans and Hispanics are less likely to report use of public libraries (D’Elia 1993). There is also evidence that these populations are less likely to utilize museums and science centers.

56 Early Education

Community and Society Community institutions as well as users must share responsibilities for building community infrastructures that support young children. Strong Families, Strong Schools urges families to increase the demands on institutions as a strategy for effective collaboration. Besides schools, libraries, and museums, many institutions within the community can provide support to early science/mathematics learning and competency. These include community-based and youth-serving groups, recreational programs, colleges and universities, community centers, and churches. The goal is to provide school-age children with experiences that connect school to real-world activities and careers; to support learning outside of school as a lifelong habit; and to explore the range of one’s potential, interests, and abilities. At least for older children (ages eleven to fourteen) research has documented differential availability of neighborhood activities, with many more options present in high-income communities, based in part on ability to pay. It may also be related to perceived (or real) considerations about safety in certain communities. The lack of community-based activities combined with a lack of enriching summer school programs produces a net loss of cognitive gains from the previous school year for poor and minority children. Television viewing, one of the main out-of-school activities in which minority participation exceeds national averages, provides few programs that are entertaining, educational, and supportive of science and mathematics learning. Sesame Street, aimed at preschoolers, began to incorporate some science into the series in the mid-1980s with the introduction of Dr. Nobel Price; however, the stereotypical portrayal of the scientist that this character represented raised many concerns. Numeracy has been a stronger aspect of this program for a longer period of time. Other science and mathematics television programs, such as Square One, 3-2-1 Contact, CRO, New Explorers, and Voyage of the Mimi, target a somewhat older audience and incorporate underrepresented groups into the program. For example, AAAS and other organizations assisted Children’s Television Workshop in identifying scientist role models even in the early 1980s. But many are questioning whether television and other media have a larger role to play in education, especially for younger viewers. A recent phenomenon, the emergence of children’s radio, may offer another media option. AAAS has developed a children’s science radio drama, Kinetic City Super Crew, aimed at eight- to eleven-year-olds. This programming was deliberately constructed to incorporate best practice, support problem-solving strategies, embed opportunities for hands-on activity, give students opportunities to provide feedback, and model equitable interaction.

Supporting Our Natural Scientists Those of us in science education often refer to young children as “natural scientists” because they use their senses to gather information about their environment, and they are naturally curious. Starting Points summarizes the characteristics of competent three-year-olds, including their intellectual inquisitiveness: they “enjoy the

Entering the Education Pipeline 57

challenge of learning new facts, skills and ways of understanding the world” (Carnegie Task Force 1994). In a Daedalus article entitled “Nature Closely Observed,” David Hawkins remarked, “The kind of experiential background in children’s lives before schooling begins, or along the way, is more uniformly adequate to math and science than to most other school subjects. The poverty or riches of social background matter less here in the early years than in other school subjects. Math and science should therefore be the great equalizers, whether they are now seen to be or not.” Competence in young children is built through their interactions with adults in home and community. Efforts to formalize the introduction of science and mathematics experiences into early childhood programs became a part of the equity movement in the mid1980s. A special session was convened as a part of the Annual Meeting of the National Association for the Education of Young Children. What Will Happen If… Young Children and the Scientific Method was published by Educational Equity Concepts in 1985. The National Science Teachers Association published a collection of articles, Early Childhood and Science (McIntyre 1984). Later in the decade, the National Urban League developed a collaborative project to devise materials, training models, and parent-coordinated activities to fit into early childhood programs such as those operated by minority- and community-based organizations. Other opportunities exist to incorporate these science-oriented projects into programs whose goals are more heavily focused on literacy, general education, and social development. Evaluation of hands-on science curricula from the 1960s, such as SCIS, showed strong results for students from disadvantaged communities and enhanced their literacy skills as well.

The Research Landscape In 1987 a review of research on the effects of different levels of the educational system on the achievement of minority students was prepared for the National Research Council Committee on Research in Mathematics, Science and Technology Education (Cole and Griffin). This review relied heavily on the AAAS Equity and Excellence study, which found that the most successful intervention programs promoted connections rather than distinctions among the different aspects of students’ lives, creating a coherent learning community. When this study is coupled with a review of ERIC studies from 1988 to the present, we have a twenty-year snapshot of the research foundations available to us. From this we are able to piece together only a fuzzy picture of SMET education for minority students in the earliest years. How do we know what to do at each level and with different populations? The state of the art on youth development provides few clues since, as one report observed, what we “know” about “normal” youth development has been based on research done on white middle-class populations (Carnegie Council on Adolescent Development 1992). A sophisticated review of existing research may allow us to extract those findings that are universal or generalizable from those that have cultural dimensions unaccounted for in the study design. Some suggestive findings include:

58 Early Education

• the importance of parental involvement in education; • the importance of parents and schools on early number development, and the connection between home experiences and math skills upon entry to school; • the role of microcomputers, even for young children, in investigating mathematical relationships; • the relationship between the early social environment and children’s longterm school performance; • the effects on problem-solving ability of the language structure of word problems for Hispanic students; • the effectiveness of comprehensive strategies using bilingual, cooperative learning and active involvement of all students in a range of instructional activities to meet the needs of English-language learners; • the effectiveness of strategies to support parents’ and children’s science and math activities in affecting their attitudes and the children’s performance; • the greater effect of parents’ material and physiological resources on math achievement over family configuration (single-parent home); • the loss of ground in mathematics achievement by African American children compared to whites over the course of the first three years of school; and • the differential in access to microcomputers in the home by race and socioeconomic status. All children need more science at an earlier age. A twelve-year longitudinal study provides evidence that audiotaped science lessons given to children in primary science classes improves those children’s performance in high school (Novak and Musonda 1991). Science and mathematics learning are clearly important for achieving long-term goals and affecting life chances, and are therefore key to equity. A number of strong interventions has been devised for children under ten, including culturally sensitive pre-school curricula and family math and science activities. A number of Saturday Academies, created in 1978 by the Resource Center for Science and Engineering at Clark Atlanta University as a component of more comprehensive inventions strategies, has extended programming to younger children. Long-term, longitudinal evaluations have not been done in conjunction with most of the interventions. Government-supported longitudinal studies (such as NELS) have only recently been planned, beginning with very young student populations, and oversampling is now commonly practiced for most of the major minority groups so that data can be disaggregated by group. The exception to this tends to be for American Indian populations, whose small numbers continue to be used as an explanation for the failure to collect or report data and to conduct little research.

Developing Structural Solutions Information is available on the shortcomings of the science, mathematics, and technology education offered to minority students, but the challenge is to recognize these problems and develop a systematic response. The National Science Foundation has

Entering the Education Pipeline 59

developed initiatives for foundation support that emphasize systemic approaches to education reform. In these programs, schools play the leading role as both the object and orchestrator of reform. An alternative strategy, pursued by AAAS since 1992 (with support by the DeWitt Wallace–Readers Digest Fund), involves a community-wide comprehensive reform program wherein the community reconfigures itself from the bottom up and realigns its resources to support a holistic vision of education. Science Linkages in the Community (SLIC) offers training, materials, and strategies to increase the number and range of organizations offering out-of-school programs for minority students. Its focus on connecting grassroots, community-based organizations to science-rich organizations, and linking them to the education system, is designed to improve SMET learning options available to all families by targeting efforts at underrepresented and nonparticipating groups. One of the greatest challenges facing AAAS is the need to document and evaluate community-wide systemic programming. The three urban centers involved in this project (Chicago, Ill., Rochester, N.Y., and Rapid City, S. Dak.) represent a range of minority populations and types of communities. All three communities have chosen to incorporate interventions targeted at pre-school and primary grade students.

Changing the Face of Science The patchwork of evidence to date supports the effectiveness of early intervention; emphasizes the importance of personal health, growth, nutrition, and development; and argues for connecting school, home, and community support for SMET education in the early years. Given what we know, the following approaches are needed at the beginning of the education pipeline: • Put policies in place to ensure that all children had access to quality health care, including immunization, screenings for disabilities, and good nutrition. • Make parents aware of their role in early education and provide them with strategies to support their children’s early learning; make parents aware of the relationship between health care and learning for young children and make health care a right of every young child. • Develop community-wide approaches to providing community-based science resources to all families. • Maintain and build on the skills that students bring to school. • Offer summer enrichment programs in science and mathematics. • Develop and support quality media programs in science and mathematics for young children. • Make tools for learning (such as microcomputers) more accessible to all children by establishing public use technology facilities. Through coherent policies, programs, and practices, we have an opportunity to affect the talent pool for science and engineering and to build the science literacy skills of the nation.

60 Early Education Selected References Carnegie Council on Adolescent Development, Task Force on Youth Development and Community Programs. 1992. A matter of time: Risk and opportunity in the nonschool hours. New York: Carnegie Corporation. Carnegie Task Force on Meeting the Needs of Young Children. 1994. Starting points: Meeting the needs of our youngest children. New York: Carnegie Corporation. Cole, M., and P. Griffin, eds. 1987. Contextual factors in education: Improving science and mathematics education for minorities and women. Madison, WI: University of Wisconsin. D’Elia, G. 1993. The roles of the public library in society: The results of a national survey, final report. Evanston, IL: Urban Libraries Council. Hawkins, D. 1983. Nature closely observed. Daedalus 112(2), 65–89. Horizon Research, Inc. 1994. Science and mathematics education briefing book, vol. 4. Arlington, VA: National Science Teachers Association. Jamar, I. 1994. Fall testing: Are some students differentially disadvantaged? New Standards Project. Pittsburgh, PA: University of Pittsburgh. Malcom, S. M. 1986. Equity in science, mathematics and technology education. Early childhood is the place to start. Paper presented at meeting of National Association for the Education of Young Children, New Orleans. Malcom, S. M., M. Aldrich, P. Q. Hall, P. Boulware, and V. Stern. 1984. Equity and excellence: Compatible goals. AAAS Publication 84-14. Washington, DC: American Association for the Advancement of Science. Malcom, S. M., J. Cownie, and J. W. Brown. 1976. Programs in science for minority students: 1960–1975, AAAS Report 76-R-10. Washington, DC: American Association for the Advancement of Science. McIntyre, M. 1984. Early childhood and science. Washington, DC: National Science Teachers Association. Mullis, I. V. S., J. A. Dossey, E. H. Owen, and G. W. Phillips. 1991. The state of mathematics achievement: NAEP’s 1990 Assessment of the National and the Trial Assessment of the States. Washington, DC: Office of Education Research and Improvement, U.S. Department of Education. National Center for Education Statistics. 1994. Digest of education statistics. NCES 94-115. Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement. National Education Goals Panel. 1993. The national education goals report: Building a nation of learners, vol. 1. The National Report. Washington, DC: U.S. Government Printing Office. National Science Foundation. 1993. Indicators of science and mathematics education 1992. Washington, DC: Author. National Science Foundation. 1993. Selected data on science and engineering doctorate awards: 1993. NSF 94-318. Arlington, VA: Author. Novak, J. D., and D. Musonda. 1991. A twelve-year longitudinal study of science concept learning. American Educational Research Journal 28(1), 117–153. Oakes, J. 1990. Multiplying inequalities: The effects of race, social class, and tracking on opportunities to learn mathematics and science. Santa Monica, CA: Rand Corporation. Quality Education for Minorities Project. 1990. Education that works: An action plan for the education of minorities. Boston, MA: Massachusetts Institute of Technology. Science Magazine. 1993. Minorities in science ’93: Trying to change the face of science. Science 262(5136). Spring, B., M. Froschl, and P. B. Campbell. 1985. What will happen if…young children and the scientific method. New York: Educational Equity Concepts.

Changing the Face of Science and Engineering Entering the Education Pipeline 61 Task Force on Women, Minorities, and the Handicapped in Science and Technology. 1988. Changing America: The new face of science and engineering, interim report. Washington, DC: U.S. Government Printing Office. ———. 1989. Changing America: The new face of science and engineering, final report. Washington, DC: U.S. Government Printing Office. U.S. Department of Education. 1994. Strong families, strong schools: Building community partnerships for learning. Washington, DC: U.S. Government Printing Office. U.S. Department of Health and Human Services, Public Health Service. 1990. Healthy people 2000: National health promotion and disease prevention objectives. Washington, DC: U.S. Government Printing Office. Zill, N., and C. W. Nord. 1994. Running in place: How American families are faring in a changing economy and an individualistic society. Washington, DC: Child Trends.

joan bissell

Changing the Face of Science and Engineering Good Beginnings for the Twenty-First Century

As we enter the twenty-first century, this essay presents an overview of key issues concerning minority children’s experiences in science, mathematics, engineering, and technology during the elementary grades. It discusses new opportunities provided by extended day after-school programs, college and university summer camps, and telecommunications projects to strengthen and expand minority students’ learning experiences. It discusses research and policy implications for identifying and evaluating new avenues to enhance minority children’s early experiences and create a substrate for their future participation and success in the science and engineering work force.

The Construction of Knowledge We know that throughout childhood youngsters construct their knowledge from the experiences they have. The kinds of interactions children have with their physical and social environments significantly affect their cognitive development (Ormrod

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1995). Children are active and motivated learners, constantly engaged in constructing their understanding of the world (Bybee et al. 1989). They pass through distinct stages, and the concepts they master in the earlier stages affect their learning in later stages. In essence, early childhood experiences are the foundation for later learning (Wadsworth 1989). We know that the early years of schooling provide the basic foundation for children’s later cognitive development and academic success (Elkind 1981). During this crucial time, children begin to understand how the world around them operates, to formulate concepts upon which their later science and mathematics knowledge is built, and to develop expectations and strategies for learning that affect their future ways of knowing (Stigler, Schweder, and Herdt 1990).

What Science Experiences Do Children from Different Backgrounds Currently Have? While child-development research tells us that all children are “natural scientists,” we know that profound differences exist in their “laboratories,” “equipment,” and “science networks.” Some are encouraged as “young scientists” by their families, school programs, and outside experiences, while others simply do not have such opportunities (National Education Goals Panel 1994). Some elementary programs encourage children to make “exploring” into a purposeful, scientific activity (Blackwell and Hohmann 1991; Sullivan 1994; Lorbeer and Nelson 1996.) The National Association for the Education of Young Children (NAEYC) standards for developmentally appropriate practice, for example, encourage primary-grade curriculum goals and teaching strategies that are fully consistent with what we know about how children learn science and mathematics (Bredekamp and Rosegrant 1992). It is widely recognized that learning science and mathematics concepts early is important for all children (Project 2061, American Association for the Advancement of Science 1990). Still, we know that parents from varying backgrounds not only have access to widely differing resources but interact with their children around learning in different ways (Stigler, Schweder, and Herdt 1990). In addition, minority and lowincome children tend to have considerably less experience with technology than do nonminority and higher income children (Becker 1990, 1994; Oakes 1990a). Research on the disparities in opportunities available to children from different backgrounds tells us that in order to reach all children effectively, we need a multitude of strategies (Clewell, Anderson, and Thorpe 1992; Oakes 1990b). These should include approaches that involve extended day after-school programs, college and university summer camps, telecommunications and information technologies, the media, and community learning resources (Bissell et al. 1996). They will need to involve parents as critical instruments for change (Stenmark, Thompson, and Cossey 1986; Shields and David 1988). These are the very types of programs about which we have the least knowledge, and they represent a high priority for research. What we do not know is far greater than what we do know about effective early elementary experiences and promising strategies in science and mathematics. Given

Changing the Face of Science and Engineering Entering the Education Pipeline 63

the importance of these years as a foundation for later learning, we need to move well beyond the documentation of inequities in science, mathematics, and technology experiences that reflect children’s economic backgrounds. We need evidence on which alternative practices are working. Despite the fact that during the past decade there have been many alternative science and mathematics initiatives focused on the early years of schooling, long-term evaluation data are virtually nonexistent. These programs provide laboratories that warrant thorough investigation as a foundation for designing new learning approaches for the twenty-first century.

The Elementary Years: Extended Day Programs, School-Age Care, and Non-School Learning During the past two decades, several trends in American society have influenced the rapid growth of what are referred to interchangeably as “extended day programs” or “school-age care” (nonfamilial care arrangements for children in kindergarten through sixth grade during non-school hours). These trends include: • dramatic increases in the numbers of mothers in the labor force and, consequently, in the numbers of children who are unsupervised during non-school hours • rising fears about the health and safety risks unsupervised children may experience • a growing recognition of the value of supplementing formal K through 12 education with informal social and educational activities that enhance a child’s development. Recognition is growing among educators, child-care experts and policy makers that we are missing a significant opportunity if we do not pay attention to the 80 percent of a school-age child’s waking hours that are not spent in school (NAESP 1995). Extended day programs primarily serve children in grades K through 3; approximately 85 percent of the children served by these programs are in the early elementary grades (Seppamen et al. 1993). The ethnic composition of programs differs in relation to the population of their communities, with public programs serving a substantial number of minority children. Sixty-eight percent of children enrolled in these programs nationwide are white, 19 percent are African American, and 8 percent are Hispanic. More African American children are enrolled in programs in the South (28%), while more Hispanic children (15%) are enrolled in programs located in the West (Seppaman et al. 1993). Extended day programs are located in many different settings (often in facilities that are used for other purposes) and are sponsored by a broad range of organizations in the public and private sectors. Programs are administered by the following types of sponsoring organizations. Examples of nonprofit providers that are serving large numbers of children include Boys and Girls Clubs, YMCAs and YWCAs, 4-H Clubs, Girl Scouts and Boy Scouts, and city and county parks and recreation departments. Schools are increasingly becoming sites for extended day programs; the National Association of Elemen-

64 Early Education Type of Organization

Percent

Nonprofit Public schools Private schools State, county, local governments Church or religious groups Private organizations Private social services or youth serving agencies Other nonprofit

18 10 5 19 7

For Profit Private corporations Private schools Other for profit

29 3 2

tary School Principals recently developed standards for these programs, which it refers to as school-age child care (NAESP 1995). The NAESP Standards state: As educators, our concern for children does not start and end with the school bell. We know that children’s ability to learn is affected by what happens outside school. A 1987 Harris opinion poll asked teachers to rank seven possible causes of students having difficulty. “Children left on their own after school” was listed by 51 percent as the number-one factor. (p. 1)

Recent research indicates that minority and low-income children’s participation in quality extended day programs results in enhanced academic and social competence and self-esteem (Posner and Vandell 1994, Riley et al. 1994, Brooks and Forman 1995). Other research shows positive effects on technological literacy where this is an area of program focus (Vasquez 1996). About one-third of all extended day programs are based in schools, with half sponsored by the host school and the other half typically sponsored by community organizations (Seppanen et al. 1993). This means that schools are in a unique position to support and enhance learning, including science learning, in many of these programs.

Extended Day Programs and Science and Mathematics Learning Opportunities The growth of before- and after-school programs is a relatively recent phenomenon, with the rate of growth increasing annually (Seppanen et al. 1993). It is clear that these programs offer an important opportunity to overcome disparities in achievement between minority and majority children (Bissell 1996). There is evidence indicating that many school-age care (SAC) professionals envision a role for these programs in enhancing science and mathematics learning. Kids’ Time: A School-Age Care Program Guide (California Department of Educa-

Changing the Face of Science and Engineering Entering the Education Pipeline 65

tion 1994), a research-based planning framework available for SAC programs, recommends SAC activities that integrate science, mathematics, and such other areas as health, physical education, the visual and performing arts, English-language arts, and history and social science. Goals it recommends for science include: to develop hands-on inquiry, questioning, problem-solving, and hypothesizing skills and learn major science concepts and themes. Goals it recommends for mathematics include: to learn numbers, measurement, geometry, pattern and function relationships, logic, and algebra. A review of other extended day program materials (Sisson 1991, Albrecht and Plantz 1993, NAESP 1995, Seligson and Allenson 1993) confirms their potential benefits for enhancing out-of-school learning of science and mathematics. Many of these programs already focus attention on and represent an important avenue for enhancing learning of science and mathematics among all children, particularly among minority students (Riley et al. 1994, Brooks and Forman 1995). Extended day program resource materials tend to have a fairly consistent approach to science and mathematics. They emphasize playful learning, providing opportunities to carry on scientific and mathematical investigations leading to concept development. This perspective is captured in the following statements by Wasserman (1990): Science is everywhere around us, from the early morning fog that immobilizes the airport to the burned toast, from the flooded carburetor to the first robin of spring, from your personal home computer to the five pounds you gained on your summer vacation. Whether it’s pearls, or butterflies or popcorn, it’s science. Whether it’s the weather, toxic waste, or the moon, it’s science. Whether it’s gravity or oil spills, it’s still science. What can children do to increase their understanding of science? Everything! The options are virtually unlimited. (121) The . . . area of mathematics is a “natural” for play . . . since the learning of mathematical concepts is most successfully done with manipulative materials and through hands-on, experiential play. Virtually all beginning mathematical concepts lend themselves to such investigations: volume and capacity; shapes and sizes; symmetry; area; estimation and measurement of length, time, speed, and temperature; balancing; angles, and two-dimensional space. (139)

In California, the Department of Education has made a significant commitment to science within extended day programs for which it provides funding. It has supported the development of a model “Young Scientist” program by the University of California, Irvine (UCI), modeled after the successful Kids Investigating and Discovering Science (KIDS) campus program developed by Dr. Eloy Rodriguez. The extended day program has been implemented in sites serving large numbers of African American, Hispanic, and Asian children. The next section will evaluate the program’s counterpart on the UCI campus by examining impacts on children’s knowledge and attitudes in science as well as on parents. Policy studies make clear that extended day programs are rapidly expanding as a major social institution in the United States, and that public programs serve substantial numbers of minority children. Analyses of the extended day program literature demonstrate the good fit between the goals and designs of quality programs and

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the environments that foster learning and enjoyment of science and mathematics among elementary school children. Research and demonstration studies are warranted in which extended day programs are evaluated as mechanisms for enhancing participation and success of minority students in science and mathematics.

Campus-Based Summer Programs for Minority Children A number of college and university campuses have undertaken special science and mathematics programs for minority children. These, too, hold significant promise for better preparing minority students in mathematics and science. Limiting factors in expanding our knowledge base have included (a) little detailed descriptive information published about the successful programs, and (b) lack of evaluations to determine short- and long-term benefits. Kids Investigating and Discovering Science: A Case Study An exception is the Kids Investigating and Discovering Science (KIDS) program developed by Dr. Eloy Rodriguez at the University of California, Irvine (UCI) campus in the late 1980s. It is a nationally recognized model in which Hispanic elementarygrade children participate as young scientists on the UCI campus. The evaluation data for the program demonstrate the significant impacts on children, their families, and their classrooms. KIDS also serves both an in-service and pre-service professional development function. Teachers from schools serving large numbers of Latino children as well as Latino undergraduates interested in teaching careers work in the program, which is structured to include professional development for the teaching staff. The KIDS teaching staff provide in-service training for community-based agencies to prepare them to offer after-school “Young Scientist” programs for Latino and other minority children. The KIDS program has been evaluated using the following criteria: • “Draw-A-Scientist” and “Draw-It-Science” assessments of children’s perceptions of science • open-ended questions about science • science attitudinal rating scale • parent questionnaire • teacher’s log • teacher and administrator follow-up questionnaire Evaluation procedures involving children and parents were administered in Spanish and English. This allowed for responses in either language or a combination of both. Information is available for all children who participated in the program during the most recent three years of operation. The sample sizes increased from 100 children in the first year to 183 children in the third year. Grade levels of participation included the primary grades (K through 3) in the first year, primary and

Entering the Education Pipeline 67 Changing the Face of Science and Engineering

upper elementary grades in the second year, and grades 1 through 8 in the third year. Children’s drawings were collected at the beginning and end of the program to provide a window into children’s perceptions of the role of scientists and the nature of the scientific enterprise. Differences between children’s drawings at the beginning and end of the summer program each year reflected a greater complexity and detail, an increased understanding of the nature of science, and closer identification with scientists. Post-test drawings were typically more detailed than pre-test drawings; children’s verbal descriptions of their drawings showed a similar pattern. For the older children, the drawings focused on topics studied. The graphic representations and children’s descriptions of them typically demonstrate an increased understanding of the topic under study. An example is in an upper elementary student’s “Draw-It-Forces” representation and explanation. The pre-test drawing shows going “fast” as being related to some kind of “strong” forces that do not have specific directionality. The post-test drawing shows a well-organized representation of how gravitational, directional, and centrifugal forces affect a roller coaster and can help seat a person and then lift that person out his or her seat. Another example is an upper elementary student’s “Draw-It-Energy” picture and description. In the pre-test drawing the only energy source that is represented is electricity. The post-test drawing includes electricity but also shows wind, sun, fire, and food as energy sources. Primary-grade new participants’ pre-test responses when asked to “tell us about your drawing of a scientist” often reflected limited understanding of the role of scientists: He is making his dog well; he is friendly and playful. He is a car mechanic. He is wearing a white coat like me. (Note: Children in KIDS wear lab coats throughout the summer program.) She reads books and works with fish. She is writing on the paper. Primary-grade children’s post-test responses and those of returning children typically reflected considerably greater understanding of scientists and what they do: He’s a botanist and he cures people with his plants. My scientist is the plant tester of the lab. My scientist is dissecting a frog using a scalpel to see the parts of his body. She is a scientist that is studying flowers to find out how much bacteria it has and to see if she can use it to make medicine. She is a scientist. She does experiments. She works with a company and works with animals. One of the consistent differences between the children’s pre- and post-test descriptions of their scientists pertains to gender. The first year of the program, almost all the children drew male scientists at the pre-test stage. By the post-test stage, both

68 Early Education

girls and boys drew female as well as male scientists. In subsequent years, pre-test drawings for returning students often showed female scientists. Each year the number of children drawing female scientists was substantially larger at the post-test stage. The three open-ended science questions that were used in determining the changes during the KIDS program yielded patterns similar to those found with children’s drawings. Pre-test responses were typically brief; they tended to associate science with tangible objects or specific activities. Post-test responses showed a shift toward viewing science as a process or an act of learning. The answers at the post-test stage were typically longer, the assertions made were more complex, and the answers included such scientific processes as investigating and discovering. For example, one primary-grade child’s pre-test response to the questions “What is science?” was “Something to learn.” The same child’s post-test response was “Science is something to learn a lot about and something to question, then do experiments to know the result.” Attitudinal rating-scale responses were collected at the beginning and end of the program. Items showing statistically significant increases were: I would like to be a scientist. My friends like science. I like learning about butterflies. I like learning about computers. I like learning about mathematics. I like learning about scientists. Science is interesting. Science is fun. Overall, girls entered the program with somewhat less positive attitudes toward science than boys and showed the greatest increases in positive attitudes. By the end of the program, girls’ and boys’ attitudes were generally comparable. For example, equal percentages of boys and girls (90%) responded positively to the statement, “We learn important things in science.” Approximately 90 percent of both girls and boys reported thinking that science was valuable, liking science, liking to learn about computers, and liking to learn about scientists. Teachers and administrators in the schools KIDS participants attend during the academic year were asked a series of questions regarding the impact of the KIDS program. One question was, “How did the KIDS program affect children’s knowledge and understanding of science?” A representative response was the following: Many of the KIDS students have become the “leaders” in their classrooms in the area of science and problem-solving. With a great deal of confidence and pride, they share with their teachers and classmates the science content material presented to them at KIDS. They also ask many challenging questions—inquisitive minds. The fact that the program is bilingual not only increases students’ knowledge but also increases their ability to verbalize themselves in a “scientific” arena.

Entering the Education Pipeline 69 Changing the Face of Science and Engineering

Another question was, “Have you observed impacts of the KIDS program on participating children’s parents? What were the effects?” A typical response was the following: These have become some of our most active parents at school. They are eager to share their hopes and their plans for their students to attend college. The median level of education of our parents is third grade. Previously most did not have any conviction to the university (or understanding of it). Most of our KIDS parents are participating in out Parent Institute for Quality Education.

In addition, teachers and administrators were asked, “Has children’s participation in the KIDS program affected how or what you teach in science? Please describe changes that have taken place in your science teaching.” Responses included the following: Due to the fact that four of your teachers have been KIDS teachers, we’ve been “infected” with KIDS philosophy and focus on inquiry. It can and does work schoolwide across all areas of the curriculum. . . . Our students are actively engaged in meaningful, student-centered, performance-bound projects.

The survey responses of teachers and administrators working with KIDS children clearly communicate their perception that the program has had a major impact on participating children, their parents, and the quality and level of science instruction they now provide. While a number of programs have been undertaken by colleges and universities to enhance minority children’s participation in and understanding of science and mathematics, little research data are available on them. The case study of the KIDS program demonstrates the significant potential of these programs and the importance of systematic evaluations of their impacts on children, their families, and their schools (Bissell et al. 1996).

Elementary School Science and Mathematics Instructional Practices and Consequences for Minority Students Students’ early school achievement in mathematics relates both to their interest in science and mathematics and to their science-related experiences in and out of school (Clewell, Anderson, and Thorpe 1992). Students demonstrating the highest achievement in science and mathematics (rarely minority students) have enhanced opportunities to learn these subjects since they are the ones likely to be selected for special enrichment programs. Achievement and interest in science and mathematics in elementary school increase the opportunities of students to learn these subjects in the middle grades (Oakes 1990b). The first indications of African American and Hispanic students who are lost to the scientific pipeline appear early in elementary school. Due to relatively low scores on achievement tests, African American and Hispanic students are more likely than white students to be placed in low-ability and remedial classes rather than enriched

70 Early Education

or accelerated programs. Despite these disparities, African American elementary school students are often as or more enthusiastic than white students about science and mathematics (Oakes 1990b). For Hispanic students, one of the primary causes of poorer performance on science and mathematics tests is the impact of language. Hispanic students—especially those whose primary language is Spanish—have been found to be lower achievers in science and mathematics on some tests (Oakes 1990b). However, where students are given the opportunity to answer questions in Spanish or English, the research does not show such differences (Bissell et al. 1994). Several studies have investigated the influence of a variety of attitudinal, motivational, and self-perception factors on the achievement and participation rates of minorities in science and mathematics. Findings suggest a relationship between relatively low performance in science and mathematics and (a) stereotyping of these subjects as “the purview of white males”; (b) students’ confidence in their own abilities; (c) their perceptions of the lack of utility and relevance of science and mathematics to their lives; and (d) the failure to connect these fields to their cultures and backgrounds (Oakes 1990b). Disproportionate numbers of minority and poor students are taught during their entire school careers by the least qualified teachers. At the elementary level, this is the result of difficulties schools have in attracting qualified teachers, high teacher turnover rates, and classrooms staffed by teachers holding only emergency credentials (Oakes 1990a). Differential access to specific science and mathematics resources has also been repeatedly documented, including inequities in the number of computers available for student use and variations in the ways computers are used for different subpopulations of children (Becker 1985). Smaller percentages of children in elementary schools serving minority children actually use the computers in the schools. In addition, fewer minority and poor schools have teachers who are computer specialists. Thus, in these schools computers are more likely to be used for “drill and practice” than for challenging instructional activities (Becker 1983). Minority students experience few role models as part of their elementary experiences in science and mathematics (Raizen and Michelsohn 1994). A number of studies have shown that role models can have a positive influence on students’ attitudes toward science and mathematics by providing active encouragement and enhancing student self-confidence (Clewell, Anderson, and Thorpe 1992). Teacher expectations for African American and Hispanic children in the sciences and mathematics are lower than for other children. Expectations influence students’ achievement, creating a substantial obstacle for minority students. Teacher expectations also lead to differences in teaching behaviors, which influence achievement. The differences involve the type and amount of material taught and degree of teacher-pupil interaction, feedback, and reinforcement (Oakes 1990b). Parents’ involvement and expectations also influence elementary school children’s attitudes and achievement in science and mathematics (Levin 1989). In some studies non-Asian minority female students have reported that their parents place less value on science and mathematics learning than the parents of other students. In other studies, both male and female non-Asian minority students have reported

Entering the Education Pipeline 71 Changing the Face of Science and Engineering

less interest on the part of their parents in their continuation of mathematics courses in later school years (Oakes 1990b). A review of the research on elementary school science and mathematics instructional practices experienced by minority students and the consequences for these students reflects the considerable disparities between their experiences and those of their nonminority peers. The research on this issue is the most extensive of any topic pertaining to minority children’s early science and mathematics learning experiences. The situation suggests three conclusions: (a) the need for systemic reforms in the delivery of elementary grade science and mathematics experiences for minority children; (b) the need to recruit and train an outstanding cadre of minority elementary school teachers with expertise in science and mathematics teaching; and (c) the need to utilize nonschool opportunities as a vehicle for fostering both learning and enjoyment of science and mathematics among minority students. The National Geographic Society Kids Network: An Exception It is important to recognize that there are exceptions in the form of elementary school science and mathematics programs that have been highly effective with minority children. One such program that warrants examination is the National Geographic Society (NGS) Kids Network program. The NGS, under funding from a number of foundations, adapted this innovative science, mathematics, geography, and telecommunications program to serve Latino children and their families. The NGS Kids Network bilingual adaption builds on the program’s six-week science units, which are an enhancement to a school’s science curriculum and are designed for children in grades 3 through 6. Children in these grades are engaged in “doing” real science: they collect and interpret data from their own experiments, share their findings with teammates from schools throughout the United States and the world, and analyze trends and patterns from data collected by all participating sites. A three-year evaluation of the bilingual adaptation by UCI focused on the HELLO!/¡HOLA! unit. This unit introduces Latino children to the scientific process, prepares them to use computers and telecommunications networks to perform research tasks and communicate with others, and gives them a “leg up” for successful participation in science, mathematics, and technology. The NGS Kids Network program emphasizes learning science by having children engage in real scientific study with others. Students work as scientists, collaborating with other students at their own sites and with students from other schools in their research teams. In addition, they interact via telecommunications networks with scientists who serve as expert resource persons for each of the program’s units. NGS Kids Network has an advanced, demanding curriculum. It overcomes the pattern in which learning activities and materials for language minority and limitedEnglish proficient students are remedial in nature, reducing the students’ opportunities for higher levels of academic achievement (Levin 1989). The program features complex concepts and problems, use of materials that enable children to draw upon and learn in their primary language, and an inter-

72 Early Education

disciplinary approach. It builds skills in mathematics, language arts, and technological literacy as part of a child’s active scientific investigation and communication. Its interactive, hands-on approaches reflect what are known to be the most effective practices in elementary science education (Bybee et al. 1989). NGS Kids Network gives students real-world problems to solve similar to those currently being dealt with by scientists studying such topics as atmospheric chemistry, water pollution, weather patterns, and solid-waste management. For example, children study acid rain by collecting water from local streams and rivers, testing its acidity, and relating their findings to causes of environmental pollution. The curricula created by the program have the attributes described by Garcia (1992) as necessary for fostering achievement among linguistically and culturally diverse students. They provide an integrated curriculum that gives children the opportunity to study a topic in depth and to apply skills acquired in a variety of contexts (e.g., involving school, home, and community). They relate academic content to the child’s own environment and experience. They involve active rather than passive learning, including group projects, in which students interact and exchange ideas with other students and the teacher. According to Garcia, “We should not lower our expectations for these students: they . . . need to be intellectually challenged” (p. 7). The child’s culture and language are meaningfully integrated into the programs in order to enhance student learning and self-concept. The program recognizes that for large numbers of minority children, the use of instructional materials that reflect their homelands contributes to the development of a positive self-image, the sense of self within the classroom, and the perception that they and their culture are valued (Nieto 1996). NGS Kids Network includes Latin America within its activities in world geography and has participating schools from this region. The HELLO!/¡HOLA! unit examines pets in different locations and environments, with pets of children in Mexico given special attention. The NGS Kids Network adaptation is bilingual, in English and Spanish, with Family Nights conducted in Spanish, thus communicating a significant message to children and parents about the value of their language and culture. The program is consistent with the work of Cummins (1991), Merino (1991), and McLaughlin (1992), demonstrating the academic and psychological benefits of using the first language. During Family Nights, parents and children participate in collaborative learning activities, such as locating the family’s hometown on maps and globes, working together on the computer (this is the first time most of the parents have ever used a computer), and using telecommunications networks. Parents play active roles in planning Family Nights and attendance has generally been very good. The NGS Kids Network program was designed to combat the poor quality of scientific and technological education that most Latino and other underrepresented minority students encounter in schools. The UCI three-year evaluation of the NGS Kids Network bilingual adaptation has demonstrated that it has had a significant positive effect on Latino children’s understanding of science, their attitudes toward

Entering the Education Pipeline 73 Changing the Face of Science and Engineering

learning science, and their interest in being a scientist. The evaluation demonstrated that the program included Latino parents in meaningful ways and showed measurable impacts on their perceptions concerning (a) scientific fields accessible to their children and (b) the contributions they can make to their children’s learning science and mathematics (Bissell et al. 1994, 1996). The NGS Kids Network bilingual adaptation for Latino elementary school students includes several instructional units, each of which operates over a six-week period during the school year. The three-year evaluation examined the implementation of HELLO!/¡HOLA!, the introductory unit in which children learn principles of science, mathematics, geography, and telecommunications by studying the tapes and numbers of pets owned by children throughout the United States and the world (Bissell et al. 1994, 1996). The evaluation included an assessment of knowledge and skills in telecommunications, science, math, and geography, as well as ten items measuring attitudes toward science and science careers. Both components showed statistically significant differences between children in the program and comparison children (Bissell et al. 1994, 1996). Parents of children participating in the NGS Kids Network bilingual adaptation were consistently positive about the effects of the program and the opportunities it provided for their participation. Parents’ positive attitudes toward the program were summed up by one mother, who explained, “My son like to go to school and never likes to miss class, especially when they do experiments and work with the computers.” Parents indicated that they would like to be more involved with their children’s school and would like more programs like the NGS Kids Network’s Family Nights: I wish there were more activities like this, in which working parents could participate out of work hours. I enjoyed seeing the kids work on the computers—that’s a big step for them. I am very interested to learn more about the technology that is available in the classroom.

Teachers participating in the NGS Kids Network bilingual adaptation were consistently positive about its hand-on and cooperative learning, its benefits to the students, and its empowering both students, teachers, and parents to creatively use and expand upon authentic, challenging instructional activities. A teacher whose response was typical of others stated: “I love it. I believe it should be incorporated across the curriculum in all school districts throughout the nation. Potential for learning and empowerment of students and their families is unlimited.” In summary, the NGS Kids Network bilingual adaptation has provided a “leg up” for Latino children in science and technology by enabling them to participate fully in a telecommunications-enhanced and inquiry-oriented science program at their school sites. Data from the evaluation of the program indicate that children participating in it have significantly enhanced their knowledge of and attitudes toward science, technology, and telecommunications.

74 Early Education

Conclusions This chapter has discussed central aspects of our knowledge base regarding early elementary school experiences in science and mathematics for minority children. While it reveals significant inequities in the experiences available to children in conventional in-school programs, it also demonstrates the considerable promise of alternative programs. After-school extended day programs, summer college and university programs such as Kids Investigating and Discovering Science, and interdisciplinary science and mathematics programs such as the NGS Kids Network program all hold significant promise for laying the foundations during the elementary grades to enable minority children to pursue and become leaders in science-based careers. The most important finding is that a number of alternative science and mathematics programs are achieving their objectives and are providing exceptional early opportunities for Latino children in science. They appear to have had substantial positive effects on minority children’s knowledge and skills in science, their attitudes toward science, their desire to learn more science, and their interest in scientific careers. They offer great promise for the development of a new generation of minority scientists. Research and Policy Recommendations The following research and policy recommendations deriving from the findings presented in this chapter should be recognized as national priorities and implemented. Elementary-level opportunities for the learning of science and mathematics among minority children need to be systematically chronicled and evaluated. The research findings point to how little is known about the opportunities available to minority children to experience science and mathematics, including alternative as well as school-based learning environments. As a basis for setting policy, information on these opportunities should be systematically collected and disseminated. Partnerships must be created and evaluated that draw upon a wide variety of resources, including those afforded by communities, to enhance learning experiences during the elementary years for minority children in science and mathematics. The research indicates that many minority children have few formal high-quality in-school learning experiences in science or mathematics. To enhance the learning opportunities available to these children, partnerships accessing the full array of community resources should be established and systematically evaluated. Bilingual learning and assessment opportunities in science and mathematics should be provided and evaluated for limited-English proficient children. Examples of successful programs for limited-English proficient Hispanic children include those in which learning materials have been available to children in their primary language and English. Consistent attention should be given to creating and studying opportunities in which children can use their primary language during the early childhood years to learn science and mathematics.

Entering the Education Pipeline 75 Changing the Face of Science and Engineering

The increasing numbers of after-school, extended day programs serving minority students should be used and studied as a resource for enhancing these children’s learning of science and mathematics. There continues to be substantial growth in the number of extended day programs serving elementary school children; these programs are delivered by a wide variety of agencies. They represent an important opportunity for enhancing minority children’s learning in science and mathematics and should be drawn upon for these purposes and studied to assess their impacts. Colleges and universities should be encouraged to create and evaluate special summer science and mathematics programs for minority elementary school children. A number of campuses have established summer science programs serving low-income minority children, and reports of success are highly encouraging. More campuses should be encouraged to undertake and evaluate such programs. A national effort should be undertaken to train a new cadre of minority elementary school teachers with expertise in science and mathematics. The current pool of teachers in grades K through 6 is not prepared to meet the special needs of minority children or to foster their understanding and appreciation of science and mathematics. A major national effort should be initiated to train a new cadre of teachers to achieve these goals and its impacts need to be evaluated. Annual reports should be undertaken to study the conditions of elementary science and mathematics instruction for minority children in the United States. The instruction many minority children have received in science and mathematics during the past two decades has been of poor quality. It is essential that a monitoring system be put in place and annual reports issued to track progress in overcoming long-standing problems. A systematic research agenda needs to be created and carried out to focus on minority children’s early childhood learning opportunities in science and mathematics. Relatively little research has been conducted on early childhood learning opportunities in science and mathematics available to minority students, and the benefits of these experiences. A research agenda, including those items discussed in this chapter, must be created and carried out. It should focus attention on programs involving parents in a variety of roles and include the development and designation of a set of valid and consistent evaluation measures to be applied across programs.

References Albrecht, K. M., and M. C. Plantz. 1993. Developmentally Appropriate Practice in SchoolAge Child Care Programs. 2nd ed. Dubuque, IA: Kendall/Hunt. Becker, H. J. 1983. School Uses of Microcomputers: Reports from a National Survey. Baltimore, MD: Center for Social Organization of Schools, Johns Hopkins University. ———. 1985. How schools use microcomputers: Results from a national survey. In M. Chen and W. Paisley, eds., Children and Microcomputers: Research on the Newest Medium, 87–107. Beverly Hills, CA: Sage.

76 Early Education ———. 1990. Computer use in United States schools, 1989: An initial report of U.S. participation in the I.E.A. Computers in Education Survey. Paper presented at the annual meeting of the American Educational Research Association, Boston. ——— . 1994. Analysis and Trends of School Use of New Information Technologies. Irvine, CA: University of California, Department of Education. Bissell, J. S. 1996. School-Age Care in California: Addressing the Needs of Children, Families and Society. Sacramento, CA: California Department of Education. Bissell, J. S., H. J. Becker, S. Simpson, M. Torres, J. Gilmore, and N. Minear. 1994. National Geographic Kids Network and Language Minority Students: The Use and Adaptation of the HELLO! Telecommunications Unit in California Public Schools. Irvine, CA: University of California, Department of Education. Bissell, J. S., R. S. Simpson, N. Minear, and M. Whitaker. 1996. Early bilingual science and technology opportunities: Promising strategies for enhancing educational achievement of Latinos in the sciences. UC Latino Eligibility Study Report. Oakland, CA: University of California. Blackwell, F., and C. Hohmann. 1991. High/Scope K–3 Curriculum Series: Science. Ypsilanti, MI: High/Scope Press. Bredekamp, S., and T. Rosegrant, eds. 1992. Reaching Potentials: Appropriate Curriculum and Assessment for Young Children. Vol. 1. Washington, DC: National Association for the Education of Young Children. Brooks, P. E., and R. Forman. 1995. LA’s Best: An After-School Education and Enrichment Program (Final Evaluation Report). Los Angeles: Center for the Study of Evaluation, UCLA Graduate School of Education. Bybee, R. W., C. E. Buchwalk, S. Crissman, D. Heil, P. J. Kuerbis, P. C. Matsumoto, and J. D. McInerney. 1989. Science and Technology Education for the Elementary Years: Frameworks for Curriculum and Instruction. Andover, MA: National Center for Improving Science Education. California Department of Education. 1994. Kids’ Time: A School-Age Care Program Guide. Sacramento, CA: California Department of Education. Clewell, B. C., B. T. Anderson, and M. E. Thorpe. 1992. Breaking the Barriers: Helping Female and Minority Students Succeed in Mathematics and Science. San Francisco: Jossey-Bass. Cummins, J. 1991. The role of primary language development in promoting educational success for language minority students. In Schooling and Language Minority Students: A Theoretical Framework, 3–29. Los Angeles: California State University; Evaluation, Dissemination and Assessment Center. Elkind, D. 1981. Children and Adolescents: Interpretive Essay on Jean Piaget. 3rd ed. New York: Oxford University Press. Garcia, E. 1992. Education of Linguistically and Culturally Diverse Students: Effective Instructional Practices. Santa Cruz, CA: National Center for Research on Cultural Diversity and Second Language Learning. Levin, H. 1989. Accelerated Schools: A New Strategy for At-Risk Students. Bloomington, IN: Consortium on Educational Policy Studies. Lorbeer, G. C., and L. W. Nelson. 1996. Science Activities for Children. Vol. 1. 10th ed. Dubuque, IA: Brown and Benchmark. McLaughlin, B. 1992. Myths Every Teacher Needs to Unlearn. Santa Cruz, CA: University of California, National Center for Research on Cultural Diversity and Second Language Learning. Merino, B. J. 1991. Promoting school success for Chicanos: The view from inside the bilingual classroom. In R. R. Valencia, ed., Chicano School Failure and Success: Research and Policy Agendas for the 1990s, 119–148. New York: Falmer Press.

Entering the Education Pipeline 77 Changing the Face of Science and Engineering National Association of Elementary School Principals (NAESP) and Wellesley College School-Age Child Care Project. 1995. Standards for Quality School-Age Child Care. Alexandria, VA: NAESP. National Education Goals Panel. 1994. The National Education Goals Report. Washington, DC: U.S. Government Printing Office. Nieto, S. 1996. Affirming Diversity: The Sociopolitical Context of Multicultural Education. 2d ed. New York: Longman. Oakes, J. 1990a. Multiplying Inequities: The Effects of Race, Social Class, and Tracking on Opportunities to Learn Mathematics and Science. Santa Monica, CA: Rand Corporation. ——— . 1990b. Lost Talent: The Underparticipation of Women, Minorities, and Disabled Persons in Science. Santa Monica, CA: Rand Corporation. Ormrod, J. E. 1995. Educational Psychology: Principles and Applications. Englewood Cliffs, NJ: Merrill. Posner, J. K., and D. L. Vandell. 1994. Low-income children’s after-school care: Are there beneficial effects of after-school programs? Child Development 65: 440–456. Project 2061. American Association for the Advancement of Science. 1990. Science for All Americans. New York: Oxford University Press. Raisen, S., and A. Michelsohn, eds. The Future of Science in Elementary Schools. San Francisco: Jossey-Bass, 1994. Riley, D., J. Stinberg, C. Todd, S. June, and I. McClain. 1994. Preventing Problem Behaviors and Raising Academic Performance in the Nation’s Youth: The Impacts of 64 SchoolAge Child Care Programs in Fifteen States Supported by the Cooperative Extension Service Youth-at-Risk Initiative. Madison, WI: University of Wisconsin–Madison. Seligson, M., and M. Allenson. 1993. School-Age Child Care: An Action Manual for the ’90s and Beyond. 2d ed. Westport, CN: Greenwood. Seppanen, P., J. Love, D. deVries, L. Bernstein, M. Seligson, F. Marx, and E. Kisker. 1993. National Study of Before- and After-School Programs. Portsmouth, NH: RMC Research Corporation. Shields, P. M., and J. L. David. 1988. The Implementation of Family Math in Five Community Agencies. Berkeley: Lawrence Hall of Science, University of California. Sisson, L. 1991. Kids Club: A School-Age Program Guide for Directors. Nashville, TN: School Age Notes. Stenmark, J. K., V. Thompson, and R. Cossey. 1986. Family Math. Oakland, CA: University of California. Stigler, J., R. Shweder, and G. Herdt, eds. 1990. Cultural Psychology. New York: Cambridge University Press. Sullivan, V. 1994. Students learn by doing in science program. California Agriculture 48 (December): 33. Vasquez, O. 1996. A model system of institutional linkages: Transforming the educational pipeline. Report of the UC Latino Eligibility Task Force. Oakland, CA: University of California. Wadsworth, B. J. 1989. Piaget’s Theory of Cognitive and Affective Development. 4th ed. New York: Longman. Wasserman, S. 1990. Serious Players in the Primary Classroom: Empowering Children Through Active Learning Experiences. New York: Teachers College Press.

yolanda s. george

78 Early Education

yolanda s. george

Early Childhood Science Programs Science and technology centers, children’s museums, nonprofit youth groups, sciencebased associations, public television and radio groups, and corporations have all developed a host of preschool/early childhood science programs aimed at children and their families and teachers. Common features of these programs include: • development of “science-friendly” materials and programs for teachers and parents of preschool and elementary-age children. Many teachers and parents do not have strong backgrounds in science. • development of age-appropriate science activities that foster science process skills such as exploring and observing, wondering and questioning, and sorting and classifying. Table 1 provides some clues as to how children aged three through twelve view the world. Table 2 outlines science process skills that help preschool children begin to think like scientists. • selection of science topics that are familiar to children. Table 3 provides ideas for preschool topics. • use of inexpensive household and everyday items for science activities. The Educational Equity Concepts “Playtime Is Science” program uses items such as corn syrup, vegetable oil, and water in small bowls or shampoo bottles to help children understand the properties of different liquids (e.g., density, smell, color, and viscosity) (Sprung et al. 1990). • recognition that safety is a primary concern in preschool science programs, including the use of safety glasses and nontoxic household substances. • information on how parents can nurture their child’s interest in science, including: setting up play areas that include science toys and games; visiting science centers (museums, zoos, planetariums, botanical gardens) and libraries; viewing science television shows; listening to children’s science radio shows; and reading science books and magazines to their children. • science career information (including brochures and videos) and in some cases providing opportunities for parents to discuss careers with science role models.

Entering the Education Pipeline 79 Early Childhood Science Programs table 1 Young People and Learning Ages 3–5 Are curious, active, eager to explore Explore their surroundings in their own way, often doing things over and over Learn most through playing with real things Sort things by what they are used for or by how they look or feel Often use trial and error to solve problems and explore questions Believe that they are the center of the world and that others see things as they do Tend to focus on parts, not the whole; on now, not a sequence of events Often confuse cause and effect and what’s real and imaginary Are developing independence and want to do things for themselves Often talk to themselves as they play Are working on skills like pouring liquids, working zippers, and using crayons, paint brushes, and other simple tools Ages 6–8 Continue to be curious, active, and eager to explore Begin to use logic to solve problems and answer questions in hands-on situations Still use trial and error often Apply past discoveries and experiences to new situations Are developing an understanding of measurement, including length, amount, and time Use stories and information from books to build up their own experiences Make and test simple predictions Are more aware of other people’s views Are becoming more independent; can plan and carry out activities and return to them the next day May be very talkative; growing vocabulary helps them express ideas; discussion is part of their learning Are becoming more skillful with tools such as scissors, pencils, hammers, and sewing needles Ages 9–12 Tend to be more selective about what interests them Are developing the ability to think about and solve problems in their heads Still learn best through hands-on experiences; do experiments; collect and organize data Can begin to evaluate their own thought process and approach to solving problems Understand cause and effect Use measurement and mathematics to solve problems Are able to see relationships between objects and ideas Use information from books and resources Can plan and stay with a project over a period of days or even weeks; more willing to practice a skill until mastered Accept and learn from the views of others Begin to develop ethics and awareness of the larger community Learn to use science equipment like simple microscopes, thermometers, scales Source: Sharing Science with Children: A Guide for Parents, North Carolina Museum of Life and Science.

80 Early Education table 2 We Are All Scientists Science Process Skill

Children

Scientists

Observe

Look, touch, smell, taste, listen

Microscope, x-rays, chromatography

Experiment

Change something and watch what happens

Change and control variables

Collaborate

Partners in the classroom

Colleagues around the world

Record

Journal, scorecard

Field notes, computer

Measure

Scale, ruler, stopwatch, measuring cup

Computer analysis, calibrated apparatus

Sort and classify

Color, size, shape, weight

Taxonomic key, relevant functioning groups

Compare

Fastest, largest, farthest

Change over time, change in differing conditions

Analyze

What happens most

Statistical analysis

Share information

Class meeting, at recess (“Guess what I found out?”)

Scientific meetings, E-mail, over coffee (“Guess what I found out?”)

Source: Sharing Science: Linking Students with Scientists and Engineers, North Carolina Museum of Life and Science.

Science and Technology Centers and Children’s Museums Science and technology centers and children’s museums around the country offer a variety of science programs for preschool and elementary school children, their families, and their teachers. These programs usually include: teacher and family workshops; preschool and day care services; exhibitions; kits and loan programs; and festivals and events. An example of a museum-based preschool program is the Lawrence Hall of Science’s PEACHES (Preschool Explorations for Adults, Children, and Educators in Science) program. PEACHES provides preschool educators, child care providers, kindergarten and first grade teachers, and parents with training in science and math units for children aged three through seven. The goal of PEACHES is to improve educators’ knowledge and understanding of science and mathematics concepts and to provide them with math and science materials to be integrated into their early childhood program. Mathematics, physical science, language arts, movement, and play are carefully integrated into life science topics of interest to young children. Utilizing over twenty years of programs and materials development experience, Lawrence’s staff received grants from the National Science Foundation, the Department of Education’s Fund for the Improvement of Post-Secondary Education, and the Hewlett-Packard Company to formalize the PEACHES project. Since 1989 PEACHES has developed ten teacher guides and issues a newsletter three times a year. PEACHES offers teacher education programs at Lawrence and at school sites

Entering the Education Pipeline 81 Early Childhood Science Programs table 3 Science in Everyday Life Animals

Watch a spider spin its web; take your pet to the vet; watch how a caterpillar changes into a butterfly; watch a mosquito as it bites; look for animal tracks in the mud or snow; act out how different animals move

Weather

Watch the sky; look at weather maps in newspapers; read thermometers; choose appropriate clothing; fly a kite in the wind; dry clothes on a clothesline; splash around in puddles; look for signs of seasonal changes; ask family and friends about the climates where they live; watch TV weather reports; ask older relatives about the worst weather they can remember; keep a weather diary for one month

Energy and conservation

Replace flashlight batteries; find your electric meter and measure how much electricity your family uses; recycle household materials; experiment with kitchen magnets; rub a balloon on your hair to create static electricity; identify and use kitchen tools; save water; ride a bike instead of taking a car ride; see how far a marble will roll

Earth and space

Compare different street surfaces; observe changes in the moon’s shape in the sky; notice the variety of building materials; read about NASA’s space program in the library or newspaper; read maps of all kinds; make model airplanes and boats; collect rocks and group the ones that are similar; see how shadows change during the day; enjoy a sunset

Plants

Plant seeds in a window box; sort vegetables and fruits; grow mold on bread; identify trees; care for house plants; create a compost pile; collect all kinds of seeds; sprout lima beans and other “kitchen” seeds; examine parts of a flower; adopt a tree and record seasonal changes; take a walk in a park

Physical and chemical properties

Measure and mix ingredients while cooking; sink and float toys in the tub; oil the hinges on a squeaky door; make and play musical instruments; dissolve sugar in hot and cold tea; grow and examine salt and sugar crystals; blow soap bubbles; sort objects (leaves, shells, rocks); turn water into ice and steam; find and compare plastics in your home; compare clothing fabrics; create heat in three ways; bounce light with mirrors

Source: Sharing Science: Linking Students with Scientists and Engineers, North Carolina Museum of Life and Science.

nationwide. It also presents workshops at dozens of local, state, and national early childhood conferences each year (Lawrence Hall of Science 1996).

Nonprofit Youth Organizations Since 1982 Educational Equity Concepts, located in New York City, has been providing mathematics and science training programs for preschool teachers and early childhood educators. Utilizing their early project and publication, the group has developed its current program, “Playtime is Science” (Sprung, Froschl, and Campbell 1985; Sprung, Colon, Fruschl, and Jenoure, 1990). This program enables preschool and early childhood educators to go beyond the classroom in order to help parents learn how to incorporate science and scientific thinking into their children’s daily routines. It also encourages parents to take an active role in their child’s education

82 Early Education

as program facilitators, teacher leaders, and/or organizers of special events such as “Super Science Saturday.” With funding from the National Science Foundation (NSF), staff from both Educational Equity Concepts and the Children’s Museum of Boston teamed with the National Urban League to implement and produce a preschool science curriculum (Webb 1995). Another nonprofit, youth-based organization, the National Easter Seal Society, has launched a children and family science program called “Access Science.” This NSF-funded program enhances the traditional classroom experience of elementary and middle school students with disabilities through science sessions, career mentoring, and community outreach. Inaccessible labs, nonadaptive equipment, and scheduling conflicts often make it difficult for teachers to successfully integrate disabled children into their classrooms. Children with disabilities and their families gather monthly at Easter Seal sites in Virginia and the District of Columbia to conduct experiments developed around science-related themes such as electricity, physics, or chemistry. Because all of the activities use inexpensive household items, families are encouraged to duplicate the experiments at home with extended family members and friends. The curricula used in the workshops were selected and adapted by the American Association for the Advancement of Science (AAAS) and an advisory group of scientists with and without disabilities. Research assistants—Easter Seal clients with various types and degrees of disability—test all activities and equipment prior to use in a workshop. Hands-on activities are supplemented by occasional field trips within the local science community. “Access Science” families are introduced to disabled professionals in science, mathematics, and technology; they thus learn about the many science-based opportunities that exist in the work force (Easter Seal Society Newsletter 1996).

Science-Based Professional Associations The American Chemical Society (ACS) sponsors two publications aimed at young children and their families. Wonder Science, edited by James Kessler and produced with the cooperation of the American Institute of Physics, is a hands-on science magazine published eight times a year. In Apples, Bubbles, and Crystals: Your Science ABCs each letter of the alphabet introduces a hands-on science activity (Bennett and Kessler 1996). AAAS has also developed two preschool and early childhood initiatives: Science Linkages in the Community (SLIC), “In Touch with Preschool Science Workshop” and “Kinetic City Super Crew” (a children’s radio adventure show). The workshop is aimed at preschool and Head Start teachers and youth leaders in after-school tutorial programs in churches and community centers, scout troops, big brother/big sister programs, summer camps, and sports and recreation groups. Topics include water and colors, shapes and designs, and motion in science. Workshops are offered at AAAS four times per year and are presented at school sites and early childhood conferences. “Kinetic City Super Crew” is a weekly, half-hour drama currently aired on fifty radio stations. A high-speed train takes the Super Crew (consisting of four teens) and

Entering the Education Pipeline 83 Early Childhood Science Programs

their computer friend Alec all over the world. Each week they solve science mysteries by using deductive logic, research, on-site investigation, and teamwork. Supplemental outreach materials include: a science magazine; weekly summary guide and activities for each show; hands-on experiments on postcards; teacher kits; a www home page; and a toll-free number.

Corporations “Science in the Summer,” sponsored by the SmithKline Beecham Foundation, introduces students in the second through sixth grades to the fun of science. Short, experiment-oriented courses are taught by certified science teachers at participating public libraries. Classes of fifteen students in two age groups (those entering the second and third grades and those in the fourth through sixth grades) are enrolled in one of six courses (bioscience, chemistry, dream machines, oceanography, paleontology, and physical science/electricity). Each course consists of four classes, each lasting forty-five minutes, held within one week. Students register for the program by signing up at their local library. The “Science in the Summer” program was conceived and developed by Dr. Virginia Cunningham, a SmithKline Beecham scientist. It has operated since 1987 through Montgomery County–Norristown Public Library in Pennsylvania, and was expanded to surrounding counties in 1993, including Bucks County, Chester, Delaware, and Philadelphia. In 1996 the program reached over five thousand children in over ninety libraries.

References Bennett, A., and J. Kessler, eds. 1996. Apples, Bubbles, and Crystals: Your Science ABCs. New York: McGraw Hill. Easter Seal Society Newsletter. 1996. Chicago. IL. George, Y., S. Malcom, V. Worthington, and A. Daniel. 1995. In Touch with Preschool. Washington, DC: American Association for the Advancement of Science. Lawrence Hall of Science. 1996. Connections: A Newsletter for Donors and Friends of Lawrence Hall of Science. Berkeley, CA. Sprung, B., L. Colon, M. Froschl, and S. Jenoure. 1990. Playtime Is Science: Implementing a Parent/Child Activity Program. New York: Educational Equity Concepts. Sprung, B., M. Froschl, and P. Campbell. 1985. What Will Happen If . . . Young Children and the Scientific Method. New York: Educational Equity Concepts. Webb, Michael, ed. 1995. An Invitation to Discover. New York: National Urban League.

anthony ward

84 Early Education

antony ward

Obstacles to Policy Formation As Malcom and Anderson (in this volume) have noted, Starting Points, the report of the Carnegie Task Force on Learning and the Primary Grades (1994), caused quite a stir by alerting the American public to the importance of good nutrition and environment for proper brain development in the period after birth. I think people now realize that this is not a period when the child is a kind of small vegetable that can be carried around. The lifelong process of learning is well underway and at a crucial stage during this early period. What we are striving for is the alignment of all of the elements in a child’s development: the prenatal and postnatal care the child receives, family conditions, the kind of preschool and elementary school the child will attend, and the role of community institutions, ranging from educational museums and libraries to social and recreation groups, and religious organizations. Television, too, can be a major force in stimulating a child’s learning process. All of these elements together form what Malcom and Anderson call a coherent learning community for children during the early years. I shifted, at an early stage in my career, from focusing on elementary education to being primarily involved with preschool programs. That experience taught me the importance of quality preschool programs, especially for their role in nurturing scientific interest in young children. Well-trained early childhood teachers recognize that young children are natural scientists, and they bring that awareness to their classrooms. In early childhood education, learning is unitary; it is not broken into departments, with separate sessions for math or geography. Young children spontaneously engage in a tremendous variety of activities, each having multiple learning aspects. One of the great pleasures in watching a three-year-old work with blocks is that the learning that is going on is so transparent. You can observe the child taking in great gulps of knowledge of how the world works, starting with social skills. How do I find an area on this floor that I can use to build my blocks and keep the child next to me from interfering? Or get the child to work with me? Working with blocks also involves problem-solving: To accomplish what she wants, the child must discover that two small rectangles form one big rectangle and two squares form a small rectangle and four squares form a large rectangle. When there is no big block left to form the other side, you can watch a small child work out the mental problem. Children do not always articulate their ideas, but in the presence of a good teacher and other children in the group, they will learn to do so. The teacher may ask,

Entering the to Education Pipeline 85 Obstacles Policy Formation

“What do we call this? Would you like to tell a story about it? I will write down the story for you.” This is not a unique practice; it is quite common in good early childhood programs. More formal activities are sometimes used for science education. I remember visiting a class of five-year-olds who were raising rabbits. The teacher had turned the growth of these baby rabbits into an elaborate science lesson. Every Friday, at a certain time, the teacher helped the children to measure the bunnies’ ears and feet, using strips of colored paper. Each week they used a different color, and after four or five weeks, they were able to create a bar graph charting the growth of the rabbits’ ears and feet. Science education is an important feature of every good early childhood program. The problem is that quality preschool programs are not universally available in this country; far from it. One challenge we face in the years ahead is to universalize these programs. What are some of the major obstacles that we are likely to encounter? Poverty and racism still head the list. The United States has the widest range of incomes, the widest gap between low and high incomes, among all the developed countries, with the exception of France. We have a higher percentage of children living in poverty than any other developed nation, with the exception of South Africa. Studies show that children of low-income families tend to be kept home alone more than children of middle- and upperincome families, who are allowed to play in the streets and playgrounds with their peers. In inner city neighborhoods, parents are understandably more fearful of their children’s safety. Research also confirms that upper-income parents spend more on their children’s tutoring, music lessons, science clubs, and museum courses than do low-income parents, and that there is higher utilization of such enrichment programs by upper-income children. One member of the Carnegie Task Force on Learning and the Primary Grades has concluded that our schools are exclusionary because of the way they are designed. This design was developed roughly 150 years ago to serve a select group of children. Since then, we have expanded the target of education to include other groups in the population, but we have not adapted school design sufficiently to meet their needs. Even though we have long gotten over the idea that a child in a wheelchair had darn well get up those school stairs by himself, when it comes to children with other differences, there is not that sense that it is the obligation of the school to make adjustments for the children under its care. The other major policy obstacle is less obvious and less often discussed because it is an outgrowth of our country’s peculiar political structure, which combines central planning and decentralized control. Centralized government and control, consistency, and coherence represented the ideal of the well-ordered state in seventeenthcentury Europe. State control and intervention is meant to assure that society is organized in a rational way, and it has had some good consequences, for example, in Scandinavian and northern European countries, where, in fact, education is better organized and more coherent than it is in this country. The model also has its downside, as we observed in Nazi Germany. While I am not condemning the American political structure, it does create a major problem. To achieve the kind of continuity among the different elements we have been discussing—prenatal care, family con-

86 Early Education

ditions, preschool programs, elementary schools, community, TV, health agencies— to develop a coherent learning community is extraordinarily difficult. We need to recognize these obstacles and encourage local efforts to exhort the public to take action. We can encourage horizontal rather than vertical structures, that is, professional organizations, business associations, and informal networks among teachers, scientists, and technical workers. I agree with Joan Bissell’s suggestion that there should be an annual report. Given the scattered authority in this country and the lack of coordination, we need someone to keep score. As Vince Lombardi said, “If you are not keeping score, you are not playing the game.” On this issue, as on many others, there needs to be a score keeper who regularly publishes information so that we will know in what direction this headless system of many power centers is headed to achieve the kind of coherence we would like to see.

iii

THE MIDDLE SCHOOL YEARS

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beatriz chu clewell and jomills henry braddock ii

Influences on Minority Participation in Mathematics, Science, and Engineering

arly adolescence is a time of accelerated physical and psychosocial develop-

Ement where there is enormous variability in growth rates (Milgram 1992). The

social environment assumes different meanings as peer norms and peer acceptance become very important and the early adolescent seeks to move from a state of dependence on adult supervision to greater autonomy and self-regulation. Thinking and learning competencies also develop during these years, including being able to think in abstract terms, explore career interests, make moral judgments, see things from another’s point of view, and be aware of one’s own multiple roles and capabilities. A survey published at the start of this decade summarized the period as a time when young adolescents need help to answer “yes” to the key self-questions they are asking themselves: “Am I competent? Am I normal? Am I lovable and loving?” (Scales 1991). Several aspects of the changes being experienced by young adolescents should be especially relevant to educators and others interested in increasing the level and diversity of participation by adolescents in mathematics and science because they raise questions in the young person’s mind about themselves, their environment, and their future that schools should help them answer in order to move successfully through these crucial years (Scales 1991). Important insights into the role of school and classroom environments in facilitating the healthy cognitive and personal growth of early adolescents have been provided by research projects that have compared how students develop during these ages following transitions to different school levels. Eccles and her colleagues (Eccles, Lord, and Midgley 1991) have argued that many of the negative changes in young adolescents’ motivations, beliefs, and behaviors— including interest and participation in mathematics and science—observable at the time of transition to junior high or middle school are not just a coincidence of timing paralleling a particular stage of human development but reflect an unfortunate 89

90 The Middle School Years

mismatch between the changing needs of early adolescent learners and the modifications of their school conditions and environments. Until recently, most of the research and policy focus regarding ethnic and gender differences in science and mathematics participation has targeted students’ experiences during high school and college, where these differences become most pronounced. However, there is growing recognition that the basis of these later gaps in math and science participation may originate at earlier developmental periods (Catsambis 1991). In her landmark study Who Will Do Science?, Sue Berryman (1983) concludes that the available talent pool of potential scientists first emerges in elementary school and peaks before the ninth grade. During the high school years, more students leave rather than enter this scientific talent pool. Thus, it becomes critical to understand those factors that contribute to the different trajectories toward and away from science and mathematics fields followed by females and students of color during the period of early adolescence, when attitudes and interests are at their most formative point. As a result, the middle years represent an important developmental stage for encouraging females and students of color to enter the math and science pipeline, which is possible only if we can identify the factors operating within individuals, their families, and schools that promote interest and success in mathematics and science. The middle school years have been identified as the most crucial in influencing membership in the math/science talent pool. What events occur at the middle school phase of the math/science process that makes it an important influence on later participation in mathematics and science-related fields? First, these are the years that determine whether a student will enroll in an academic track—a prerequisite for access to advanced mathematics and science courses. Second, these years have been identified as the period during which minority (and female) interest in science and mathematics declines. Third, middle school years are a time of great developmental change in the psychomotor, affective, social, and cognitive domains, all requiring special educational treatment. Fourth, as students enter the eighth grade, they must review academic options in order to make decisions about course selections that will affect future career choices (Clewell, Anderson, and Thorpe 1992). In their study of high-achieving African Americans who did not continue to pursue scientific careers, Anderson and Pearson (1988) found that it was critical that interest in a sciencerelated career develop before high school in order to ensure adequate secondary preparation in mathematics and science. The purpose of this chapter is to bring together current research on factors operating during the middle school years that affect the participation of students of color in mathematics and science fields, as well as to identify effective efforts to overcome barriers to participation. In doing so, we hope to raise questions that should be addressed by future researchers and highlight major policy issues for future debate and consideration. This chapter will examine the research on middle school students of color and their attitudes toward mathematics and science, their achievement and performance

Influences on Minority Participation in Mathematics, Science, and Engineering 91

in these subjects, and their career interests and aspirations with respect to mathematics and science-related careers. Factors influencing attitudes, achievement, and career interests and aspirations will be identified as they operate in relation to individual students, their homes and families, and school-based settings. Efforts to intervene at the middle school level to address the underrepresentation of people of color in mathematics and science will be described and effective strategies will be identified. The chapter will conclude by identifying the gaps in the research and major policy issues that still need to be addressed.

Influences on Attitudes and Perceptions Regarding Mathematics/Science and Engineering The middle school years are marked by transformation, when students are developing and changing their attitudes about themselves and those around them, including their families, friends, and their world in general. Their conceptions about who they are and what they may become, what they like and dislike, and what is important to them are being formed. Throughout this period of development they begin to construct a system of beliefs and preferences—including those related to mathematics and science—that affect their choices and actions, both in the present and the future (Clewell, Anderson, and Thorpe 1992). Students’ attitudes and perceptions regarding mathematics and science represent a number of dimensions: • expressed liking, which associates enjoyment and pleasure with participation in mathematics or science activities or studies • perceived value or utility, which is the belief that science and mathematics are useful to their present and future lives • appropriateness, which is a student’s perception that these subjects are consistent with his or her own self-image as a member of a gender or racial/ ethnic group • self-concept of achievement, which is a student’s belief in his or her ability to perform mathematics or science successfully For white students, who make up the majority of the student population, positive attitudes toward science relate positively to proficiency in science (Mullis and Jenkins 1988). The same relationship exists for mathematics (Dossey et al. 1988). Middle school marks the decline of a student’s liking of mathematics and science. In response to the question, “Do you like science?,” students in the eighth grade showed a decline from the fourth grade in the percentage who admitted to liking science (Jones et al. 1992). This decline occurred for all racial/ethnic groups as well as for males and females (see table 1). With regard to attitudes toward mathematics, the decline in attitude is not as great; for African American students, there is even a slight increase in the percentage of students who enjoy mathematics (see table 2). (The terms “African American,” “Latino,” and “American Indian” will be used throughout this chapter regardless of the racial/ethnic designations employed by the various databases and studies cited.)

92 The Middle School Years table 1 Student Responses to the Question: “Do You Like Science?” Yes

No

(% of students)

(% of students)

Grade 4

80

20

Male Female White Black Hispanic Asian/Pacific Islander American Indian

81 78 81 75 76 78 80

19 22 19 25 24 22 21

Grade 8

68

32

Male Female White Black Hispanic Asian/Pacific Islander American Indian

72 64 67 70 71 70 71

28 36 33 30 29 31 29

Source: The 1990 Science Report Card: NAEP’s Assessment of Fourth, Eighth and Twelfth Grades. Prepared by Educational Testing Service under contract with the National Center for Education Statistics, March 1992.

The National Assessment of Educational Progress (NAEP) as well as research studies have found that (1) students’ attitudes toward mathematics and science generally decline as they progress through the middle school grades; (2) students of color tend to have more positive attitudes toward mathematics and science than do white students but have lower achievement levels in these subjects; and (3) sex rather than ethnicity generally seems to be a better predictor of science attitudes, with females table 2 Students’ Liking/Enjoyment of Math (1986 Mathematics Assessment)

Male Female White Black Hispanic

Third graders’ responses to: “I like mathematics” (%)

Seventh graders’ responses to: “I enjoy mathematics” (%)

True

Strongly Agree/Agree

60 60 58 61 70

54 57 54 63 53

Source: The Mathematics Report Card: Are We Measuring Up? National Assessment of Educational Progress (Educational Testing Service 1988).

Influences on Minority Participation in Mathematics, Science, and Engineering 93

of all racial/ethnic groups showing less positive attitudes toward these subjects than their male counterparts. Although most research on attitudes toward mathematics and science has associated positive attitudes with high levels of participation and performance in these subjects, this may not be the case for people of color. Several studies have shown that although African Americans and Latinos have more positive attitudes toward mathematics and science than do their white counterparts, they have lower achievement levels (James and Smith 1985; Kahle 1982; Marrett 1986; Matthews 1984; Rakow 1985; Walker and Rakow 1985). In a study of attitudes of African American and Hispanic students toward science, Rakow (1985) found that sex rather than race was a better predictor of science attitudes of nine-year-olds. Males of all three groups—African American, Hispanic, and white students—had much more positive attitudes toward science than did the females, with Hispanic females having the least positive attitudes. For thirteen-yearolds the pattern was repeated once again. This trend has continued in the most recent science assessment (1990), except in the case of Latino nine-year-olds (see table 3). An early study (Nelson 1978) investigating sex differences in attitudes toward mathematics among fifth-grade and eleventh-grade African American students concluded that the few differences that were found favored males, although no sex differences in achievement were found at the fifth-grade level. Significant relationships between math achievement and attitudes were found for fifth-grade males and eleventh-grade females. The case seemed different for gifted African American middle school students studied by Yong (1992). In this research no gender differences were found in attitudes toward mathematics and science, although significant table 3 Student Responses to the Question: “Do You Like Science?” (1990 Science Assessment) Nine-Year-Olds Yes Ethnicity and gender

Thirteen-Year-Olds No

Yes

No

Number

%

Number

%

Number

%

Number

%

2,337 1,979

81 78

533 568

19 22

2,203 1,840

73 65

828 990

27 35

633 617

76 74

198 214

24 26

478 507

73 71

178 207

27 29

334 331

78 83

93 66

22 17

388 320

74 62

136 194

26 38

White Male Female Black Male Female Hispanic Male Female

Source: 1990 NAEP Science Assessment data. These figures do not include Asian/Pacific Island, American Indian, or students of other ethnicities.

94 The Middle School Years

gender differences were found in attitudes toward success in mathematics and perceptions of mathematics as a male domain. Female students tended to anticipate positive consequences as a result of success in mathematics more than their male counterparts; female students were less likely to perceive mathematics as a male domain than were male students in the sample. The findings of a study by Creswell and Exezidis (1982) contradict those of most researchers on gender differences in attitudes toward mathematics. In this study the researchers reported that African American and Mexican American adolescent women had more positive attitudes toward mathematics than did their male counterparts. Creswell and Exezidis also found that the attitudes of African Americans were more positive than those of Mexican Americans. In general, research has reported that students of color tend to like mathematics, to find it interesting, and to have little mathematics anxiety throughout their primary and secondary education (Anick, Carpenter, and Smith 1981; Matthews 1981). For science, the research of James and Smith (1985) has revealed that the greatest decline in science subject preference and attitude takes place between the sixth and seventh grades for the population as a whole, as well as for female and African American students. The study did not disaggregate data by gender within racial/ethnic group. Various influences contribute to a student’s attitude toward and perception of mathematics and science. They can be grouped into three main categories: individual factors, such as a student’s self-concept and self-efficacy regarding mathematics and science; home/societal factors, such as parental influence, support in the home for science activities and projects, and out-of-school experiences and hobbies; and schoolrelated factors, such as interactions with teachers and peers, exposure to science instruction, and school context for science learning. Individual Factors It is important that students perceive the doing of science and mathematics as consistent with their view of themselves as members of a group. It is also important that they believe they have the ability to participate successfully in these subjects. Stereotypes about mathematics and science as being male- and white-dominated subjects are part of the intellectual environment during the middle school years. Students of color and their white classmates learn racial stereotypes that suggest that mathematics and science are “white” fields (Hall 1981; Kenschaft 1981). By the end of elementary school or the beginning of junior high school, both boys and girls begin to view the study of mathematics as “masculine” (Erickson 1987), although there is a wealth of research to indicate that this view is more likely to be held by boys than girls (Armstrong 1981; Brush 1980; Eccles-Parsons et al. 1983; Fennema and Sherman 1978; Fox 1981; Kahle and Lakes 1983). Research on middle school students of color has resulted in similar findings. A study by Yong (1992) found that gifted African American middle school girls were much less likely to stereotype mathematics as a male domain than were their male counterparts. MacCorquodale (1980) showed that Mexican American (as well as white) young women in her study were less biased than their male counterparts and thought that science was just as appropriate for women as for men. In her study of high-ability African American women, Krist

Influences on Minority Participation in Mathematics, Science, and Engineering 95

(1993) found that these women were affected more by race-related stereotyping than sex-related stereotyping of science and mathematics. Students’ self-confidence as successful doers of science and mathematics affects their attitudes toward these subjects. As table 4 shows, while third graders of both sexes and three racial/ethnic groups (white, African American, and Latino) had equal levels of confidence in doing mathematics, by the seventh grade differences among the groups began to emerge. Although males generally demonstrate more confidence than females in mathematics, there is some evidence that African American middle school–aged girls may show more math confidence than their male counterparts. In a study of seventh-grade students, white boys scored higher than white girls on the Fennema–Sherman confidence scale, but African American girls scored higher than boys of the same racial/ethnic group (Hart and Stanic 1989). In addition, African American girls in this study showed more confidence than any of the other three groups. In contrast to this study, research on African American eighth graders in an inner-city school found that boys scored higher than girls on a measure of science self-concept, although no sex differences in science and mathematics achievement were found. Boys were also significantly more likely than girls to choose a sciencerelated occupation over a nonscience occupation (Jacobowitz 1983). Whether or not students perceive science and mathematics as being useful both to society and to their own future academic and professional careers influences their attitudes toward these subjects. Students of color tend to be less likely to perceive mathematics as useful to their everyday lives; they see it as something that is done in the classroom (Matthews 1981). As table 5 shows, white seventh graders are more likely to see math as useful than are their African American and Latino counterparts. Interestingly, except for the career utility of mathematics, more females than males saw this subject as being useful (Dossey et al. 1988). African American students of both sexes entertain misperceptions about science, have fewer science experiences, find science less useful out of school, are less aware

table 4 Students’ Confidence in Doing Mathematics (1986 Mathematics Assessment) Third graders’ responses to: “I am good with numbers” (%)

Seventh graders’ responses to: “I am good at mathematics” (%)

True

Strongly Agree/Agree

Nation

65

60

Male Female White Black Hispanic

66 64 65 66 65

64 57 62 58 50

Source: The 1990 Science Report Card: NAEP’s Assessment of Fourth, Eighth and Twelfth Grades. Prepared by Educational Testing Service under contract with the National Center for Education Statistics, March 1992.

96 The Middle School Years table 5 Value of Mathematics, Grade 7 Strongly agree/agree (%) Most of mathematics has practical use Nation Male Female White Black Hispanic

80 79 81 81 78 75

Mathematics is useful in solving everyday problems Nation Male Female White Black Hispanic

74 73 76 76 69 68

When you think about what you will do when you are older, do you expect that you work in an area that requires mathematics? Nation Male Female White Black Hispanic

44 48 40 46 39 37

Source: The Mathematics Report Card: Are We Measuring Up? National Assessment of Education Progress (Educational Testing Service 1988).

of scientific methods and how scientists work, and are less confident of the ability of science to solve problems (Kahle 1982). MacCorquodales’ study (1980) of Mexican American and white student attitudes found that white women and Mexican American men perceived science as most important for an understanding of the world, but that Mexican American women rated it as less important than did white women or white and Mexican American men. Home/Societal Factors Parents’ expectations regarding their children’s success in certain subjects are a potent influence on the performance and attitudes of these children. Much more research has been done on the effect of parental influence in affecting gender differences in attitude toward mathematics and science than on parental influence as it affects the attitudes of students of color in these subjects. Although the perceptions of white parents regarding their children’s mathematics aptitudes tend to favor sons rather than daughters (Parsons, Adler, and Kaczala 1982), and white parents of girls in middle schools have low expectations of their performance in science courses

Influences on Minority Participation in Mathematics, Science, and Engineering 97

(Kahle 1983; Schreiber 1984), findings for parents of color have been different. In a study by Andrews (1989) of parents in four racial/ethnic groups—Latino, Asian American, African American, and white—it was found that African American parents were the most supportive of all groups. Asian American and Latino parents were more likely to consider mathematics as a male domain than either African American or white parents. Krist (1993) suggests that studies of the effect of significant others on the children of African Americans need to look beyond parents at other adult socializers who may be part of the extended family network. The high-ability African American women in her study found encouragement and support from the extended family, particularly their mothers and grandmothers. This study also pinpointed the importance of friends, mentors, and the community (including the church) in affecting the aspirations and expectations of African American women to persist in mathematics and science; participants in the study felt that these influences were stronger than their school experiences. Students of color have less exposure to science through extracurricular activities. Results of the NAEP science assessments continue to reveal disparities between the percentage of white students and students of color who have exposure to various types of scientific equipment and out-of-school experiences (Kahle and Lakes 1983; Mullis and Jenkins 1988; Jones et al. 1992). Rakow (1985), however, found no direct relationships between the number of science activities experienced and attitudes toward science on the part of African American and Latino students. In general, the exposure of students to role models in science has been linked to a corresponding improvement in their attitudes toward science and scientists. Smith and Erb (1986) examined the connection between exposure to women working in science careers and changes in the attitudes of early adolescents toward women scientists. Their study, which used race- and gender-mixed groups, found that both boys’ and girls’ attitudes toward women scientists improved after exposure to the role models. Although this study did not disaggregate data by race, some researchers have suggested that the lack of ethnically and sexually diverse role models in science may lead students of color to view science as a white male domain (Beane 1985; Marrett 1986). School-Related Factors A number of factors relating to the school and classroom environment affect the attitudes of students of color toward mathematics and science. The influence of teachers, peers, and others in the school setting is an important factor, as is the way people of color are represented in mathematics and science textbooks. Teacher expectation and encouragement affect both student attitudes and achievement, and teacher encouragement is strongly related to a student’s confidence in his or her mathematical ability (Erickson 1987; Sherman 1979). Matthews (1984), who examined teacher influences on mathematics attitudes of students of color, found that teachers can have a strong positive effect on attitudes. Students of color indicated that they were encouraged and assisted by teachers who worked with them, provided extra help, explained things carefully, and offered encouragement (Matthews, 1981; Treisman 1992).

98 The Middle School Years

There seem to be no studies of peer influence on attitudes of students of color toward mathematics and science. However, a study by Talton and Simpson (1985) revealed that as students progress through middle school, their own attitudes become increasingly similar to those of their peer group, reaching a peak at the ninth grade. Research cited in a previous section has shown that boys are more likely than girls to stereotype science and mathematics as a male domain. This holds true for African American and Mexican American as well as white middle school students. Table 6, which gives responses for nine-year-olds from the 1992 NAEP mathematics assessment, supports this finding. A balanced and representative portrayal of women and underrepresented groups in science and mathematics textbooks can help students of color develop positive attitudes toward these subjects and learn to see themselves as science professionals. Studies undertaken in the 1970s and early 1980s revealed that mathematics and science textbooks contained stereotypical views of women and people of color and perpetuated the common view of these fields as the domain of white males. More recent studies have found contemporary mathematics textbooks to be much less sexist and racist than those reviewed two decades ago (Garcia, Harrison, and Torres 1990). Nevertheless, representation of women and people of color in careers requiring a knowledge of advanced mathematics continues to be less than adequate. A study by Powell and Garcia (1985) found that more recent elementary textbooks do show students of color performing science activities; however, the researchers also found that the majority of the illustrations depicted white adults in science-related occupations.

table 6 Student Responses to the Statement: “Math Is More for Boys Than Girls” (1992 Mathematics Assessment) Nine-Year-Olds Agree Ethnicity and gender

Undecided

Decided

Number

%

Number

%

Number

%

484 273

11 6

834 443

19 11

3,044 3,486

70 83

253 162

22 14

203 148

18 13

700 857

61 73

118 81

17 12

136 94

20 14

428 475

63 73

White Male Female Black Male Female Hispanic Male Female Source: NAEP. These figures do not include Asian/Pacific Island, American Indian, or students of other ethnicities.

Influences on Minority Participation in Mathematics, Science, and Engineering 99

Summary of Research Findings The research shows that interest in and liking of science and mathematics decline as students progress through the educational system. It is likely that this decline occurs during the middle school years for all students. Students of color, however, may have more positive attitudes toward mathematics and science than their white counterparts, although their achievement levels are lower. Sex may be a better predictor of science and mathematics attitudes than race/ethnicity, with females showing less preference for these subjects than males in the same racial/ethnic groups. Students’ attitudes toward mathematics and science are affected by their views of these fields as appropriate for them, by their perceptions of themselves as competent “doers” of science and mathematics, and by their perceptions of these subjects as useful to both society and to their future academic and professional careers. In general, African American and Latino students, as compared to white students, are less confident about their ability to do science and mathematics and are less apt to see science and mathematics as useful and appropriate for them. Girls of all race/ ethnicities are less apt to sex-type mathematics and science than are their male counterparts. Exposure of students to role models in science improves their attitudes toward science and scientists. Exposure to female scientists improves the attitudes of both boys and girls toward women scientists. Teacher expectation and encouragement affect student attitudes toward mathematics as well as confidence in mathematics ability. Attitudes of both boys and girls become increasingly similar to those of their peer group as students progress through middle school, reaching a peak at grade nine.

Influences on Student Achievement and Performance Differences in achievement and performance between students of color—mainly African American, Latino, Asian American and American Indian middle schools students—and their white counterparts have been documented by NAEP, which tests student performance in mathematics and science (as well as other subjects) on a national level every few years. Performance data are collected for fourth graders (mostly nine-year-olds), eighth graders (mostly thirteen-year-olds), and seniors in high school (mostly seventeen-year-olds). This discussion of differences in achievement will focus on the first two age cohorts, which encompass the middle school grades and will be limited to underrepresented racial/ethnic groups, namely, African Americans, Latinos, and American Indians. Achievement in mathematics and science will be discussed separately. Ethnic and racial differences in mathematics performance on standardized tests are larger and appear earlier than do gender differences (Dossey et al. 1988; Gross 1988; Reyes and Stanic 1988). In addition, an important finding of Gross’s study (1988, 1989), especially for African American and Latino students, was that once a student falls below the standard level of performance for his or her grade level, it is unlikely that that student will ever catch up. Lockheed and colleagues (1985) reported that Asian American students outperformed white students and that both Asian

100 The Middle School Years

American and white students outperformed African American and Latino students on tests of mathematics proficiency. Asian American, white, and Latino students surpassed African American students on a number of mathematics assessments, including: NAEP; the Iowa Test of Basic Skills: Math; California Test of Basic Skills: Math; reasoning tasks; and district achievement tests. This supports Gross’s (1988) finding that Asian American and white students outperformed African American and Latino students. Gross also noted that this pattern appeared as early as grade three. As mentioned earlier, the NAEP assessments in mathematics and science are the major source of information on national trends with respect to student achievement in those subjects. NAEP results have documented the fact that by age nine African American students performed well below the national average on NAEP mathematics assessments, and until recently the difference increased with age (Anick, Carpenter, and Smith 1981; Holmes 1980; Matthews 1983). On the last two NAEP mathematics assessments (1990 and 1992), however, although the gap between white students and their African American and Latino counterparts widened between grades four and eight, there was a narrowing of the gap between grades eight and twelve (Mullis et al. 1993). In recent years students of color have been making greater gains than white students on the NAEP mathematics assessment, although African American and Latino students’ scores were still well below the national average, with most of the increases falling in the lower range of proficiency (Dossey et al. 1988; Matthews et al. 1984; Mullis et al. 1993). Results from the 1992 mathematics assessment, however, show white students making greater gains than their African American and Latino counterparts at grades four and eight (but not at grade twelve). African American eighth graders’ proficiency levels actually decreased slightly from the 1990 to the 1992 assessment (Mullis et al. 1993). The 1990 and 1992 NAEP mathematics assessments show both African American and Latino students performing below the basic proficiency levels at the fourth, eighth, and twelfth grades. White students consistently scored higher than African American and Latino students, and Latino students’ scores were higher than those of their African American counterparts (see table 7). There is less consistent evidence regarding gender differences in mathematics performance among students of color. In two separate studies published the same year, it was found that both African American and white women scored lower than their male counterparts at all levels of mathematics (Jones 1987), while the largest gender differences in favor of boys were among white and Latino students and the smallest occurred among African American students (Moore and Smith 1987). In their study of the relationship of race, class, and gender to mathematics achievement among fifth-, eighth-, and eleventh-grade students, Kohr and his colleagues (1991) observed achievement differences across grade levels for socioeconomic status (SES) and race but not for gender, with white students scoring higher than African American students. Achievement also varied directly with student SES level; high SES students had higher achievement scores than lower SES students. Nelson (1978) also reported no gender differences in mathematics achievement between African American fifth graders, but she did find that eleventh-grade boys had higher achievement scores than their female counterparts in the sample, implying that for African American students, just as for their white counterparts, the achieve-

Influences on Minority Participation in Mathematics, Science, and Engineering 101 table 7 Average Mathematics Proficiency Levels by Race/ Ethnicity and Gender in Grades 4 and 8 (1990 and 1992) 1990 average proficiency

1992 average proficiency

White Male Female

221 220

228 225

Black Male Female

189 190

192 191

Hispanic Male Female

198 198

200 201

White Male Female

271 269

277 277

Black Male Female

238 238

237 237

Hispanic Male Female

245 245

246 247

Grade 4

Grade 8

Source: Mullis et al. 1993. NAEP 1992 Mathematics Report Card for the Nation and the States. Educational Testing Service.

ment gap favoring males widened as students progressed through the education pipeline. The results of the 1992 NAEP mathematics assessment, however, suggest that this trend may be reversing itself. Although African American and white males scored higher at the fourth-grade level than did their female counterparts, scores of males and females of both racial groups were even at the eighth-grade level, while males again scored higher than females at the twelfth-grade level. Most notable, for Latino students the 1992 assessment shows females scoring higher than males at each of the three grade levels, with the gap between females and males widening at the twelfth grade. NAEP data compiled over a period of years show substantial disparities in science proficiency between racial/ethnic groups. For example, the 1990 science assessment showed large disparities in science proficiency between white and Asian/ Pacific Islander students and their African American and Hispanic counterparts. These differences occurred in each of the four content area covered by the NAEP assessment: the life sciences; physical sciences; earth and space sciences; and the nature of science. Table 8 gives the average science proficiency by race/ethnicity and gender for white, African American, and Latino students in grades 4 and 8. The levels of science proficiency utilized by NAEP are described as follows:

102 The Middle School Years table 8 Distribution of Students and Average Science Proficiency by Race/Ethnicity and Gender Percentage of students

Average proficiency

White Male Female

36 34

243 241

Black Male Female

7 8

205 206

Hispanic Male Female

6 5

213 211

White Male Female

36 35

274 271

Black Male Female

7 8

232 230

Hispanic Male Female

5 5

243 239

Grade 4

Grade 8

Source: Jones et al. 1992. The 1990 Science Report Card: NAEP’s Assessment of Fourth, Eighth, and Twelfth Graders (Office of Educational Research and Improvement, U.S. Department of Education).

Level 200—understands simple scientific principles Level 250—applies general scientific information Level 300—analyzes scientific procedures and data Level 350—integrates specialized scientific information It is interesting to note that, except for African American fourth graders, gender differences favoring males are present at both the fourth and eighth grades for all three racial/ethnic groups. During the years that NAEP has administered science assessments, some subgroups have shown more improvement than others. Recently the largest gains have been made by groups of students considered to be at risk, including Latino and African American students. Although these recent improvements have narrowed the differences across subpopulations, the gaps still remain substantial. In 1986, African American and Latino thirteen-year-olds showed an average science proficiency below that of white nine-year-olds (Mullis and Jenkins 1988). This finding was repeated in the 1990 assessment (Jones et al. 1992).

Influences on Minority Participation in Mathematics, Science, and Engineering 103

Factors that influence achievement in mathematics and science for middle school students of color can be divided into three categories: individual factors, home/ societal factors, and school-related factors. Individual Factors Regardless of race and gender, the more favorably students view the subjects of mathematics and science and view themselves as competent in these subjects, the more likely they are to do well and to persist in taking more advanced courses. There has been little research on the effect of stereotyping of mathematics and science as white male domains on the mathematics and science achievement of students of color, although Campbell (1986) has suggested that the view of math as a white activity may discourage the achievement of students of color in this subject. MacCorquodale (1988) has attributed gender differences in mathematics achievement among Mexican Americans to sex-role stereotyping, the lack of role models, and perceptions of role conflict for employed women. Rhone (1989) found mathematical self-concept to be a significant predictor of the math achievement and problem-solving ability of the adolescent African American girls and boys in his sample. In a study of gender differences in achievement, selfefficacy, anxiety, and attributions in mathematics among African American junior high school students, Lewellyn (1990) found that females in the sample outperformed males in math achievement. No gender differences were found for mathematics anxiety, self-efficacy, or attributions. The study also revealed that greater self-efficacy for both girls and boys was achieved in the eighth grade, suggesting that this was a possible developmental trend. Ninth graders in the sample also used ability attributions more than seventh or eighth graders. As with mathematics, students’ achievement in science is related to their level of confidence in their ability to do science, although the nature of this relationship is unclear (Mullis and Jenkins 1988). For students of color, gender differences in self-concept favoring boys has been found, as they have for white students. In studying Mexican American and white students of both sexes, MacCorquodale (1984) found that girls and Mexican American students of both sexes were less likely to rate themselves high on a scale of science-related characteristics such as creativity and intelligence. In a study of African American urban junior high school students, Jacobowitz (1983) found that the girls’ mean science self-concept score was significantly lower than the boys’, even though their science achievement was almost equivalent. High test anxiety has been associated with low math achievement for all students (Medin 1985), but few studies of test anxiety have focused on students of color in the middle school years. One study by Willig and colleagues (1978) found a negative relationship between test anxiety and performance among Latino students. Results of similar studies with African American students, however, have been equivocal (Payne, Smith, and Payne 1983; Willig et al. 1978). Payne and colleagues found the correlation between test anxiety and performance in science to be positive for African American male and female fourth graders but negative for white fourth graders of both sexes.

104 The Middle School Years

Research on learner characteristics and their relationship to mathematics and science learning focus on locus of control/learned helplessness, cognitive skills, cognitive abilities, persistence and independence and language background—all of which have been cited as factors influencing differential achievement in mathematics and science for people of color. Locus of control is the extent to which a person feels responsible for success or failure. A person who feels responsible for his or her success or failure is said to have an internal locus of control; the opposite is true for a person with an external locus of control (Olstad et al. 1981). Related to this is the phrase “academic learned helplessness,” which describes students who feel that they cannot control their failure. Internally oriented students have consistently and significantly higher achievement levels than externally oriented students (Witkin et al. 1977; Medin 1985). Research has suggested that students of color are more likely to have an external locus of control and white students an internal locus of control (Beane 1985; Brown et al. 1980; Rowe 1977). Cognitive style describes the tendency to process information in either an analytic or a global fashion (“field independent” versus “field dependent”) (Witkin and Goodenough 1988). Although cognitive style appears to bear very little relationship to general achievement, some research has suggested a strong relationship between high achievement in mathematics and science and field independence (Olstad et al. 1981; Roberge and Flexer 1983; Vaidya and Chansky 1980). According to research, students of color appear to be field dependent more often than white students (Buriel 1975; Ramirez and Castaneda 1974). For example, a number of studies have found significantly more field independence among white children compared to Mexican American children (Buriel 1975; Kagan and Zahn 1975; Kagan, Zahn, and Gealy 1977; Ramirez and Price-Williams 1974; Sanders, Scholz, and Kagan 1976). Ramirez and Price-Williams (1974) studied a sample of fourth-grade children and found that African American and Mexican American children were significantly more field dependent than their white peers. In another study, Kagan and Zahn (1975) compared cognitive styles and math performance of Mexican American and white students in grades two, four, and six. They found that Mexican American boys and girls were significantly more field dependent and received significantly lower scores on a standardized test of basic skills than did their white counterparts. The incidence of greater field dependence among students of color has been attributed to cultural factors, although the causes of ethnic differences in cognitive style have not been explained satisfactorily. There seems to be agreement, however, that the field dependent cognitive style of some students of color may inhibit their interest and performance in mathematics. Some researchers have argued that current approaches to the teaching of science and mathematics are not conducive to the learning of these subjects by field dependent learners. They have suggested that approaches that are more sensitive to field dependent learners should be adopted by the schools and that various teaching models are needed to address diverse styles of learning (Holtzman, Goldsmith, and Barrera 1979; Sigel and Coop 1974). Although students of color have made large gains in the areas of computational skills and mathematical knowledge, they continue to exhibit less understanding and application of concepts (Beane 1985). Witthuhn (1984) reported that American Indian and African American students had particular difficulty with numeration.

Influences on Minority Participation in Mathematics, Science, and Engineering 105

According to Coles and Griffen (1987), the widespread use of drill-and-practice in mathematics tends to bring students up to grade level on basics but does not lead them into higher-order activity. Language background and English proficiency have often been cited as factors that affect mathematics achievement and performance (DeAvila and Duncan 1980; Mestre 1981; Myers and Milne 1988; Saxe 1988). In a highly controversial study, Orr (1987) attributed the difficulty many African American students experience in mathematics to differences between Black English and Standard English, arguing that the former lacks the prepositions, conjunctions, and relative pronouns necessary to communicate quantitative mathematics concepts effectively. For many Navajo girls and boys, English is a second language and lack of facility in English can affect mathematics performance. Styles of thought and communication embedded in the Navajo language may differ from the English language–generated styles, and this may influence Navajo students in their approach to learning mathematical concepts and solving problems (Moore 1982; Smith 1981). This may explain why some Navajo students have difficulty accepting mathematical problems in which a hypothetical situation is posed; difficulty with the concept of equations may also be related to language background. Level of English proficiency rather than bilingualism seems to be the determining factor in mathematics achievement of bilingual students (DeAvila 1980; MacCorquodale 1988; Nielsen and Fernandez 1981; So and Chan 1982). Research on verbal problem-solving skills among Latino students reveals that they experience difficulty in comprehending verbal mathematical problems (Mestre 1986; Morales, Shute, and Pellegrino 1985). Although Cuevas (1984) thinks that a child who is not a native speaker of English may have problems with language constructions used in mathematics classes, Duran (1987) suggests that difficulties in mathematics learning may also arise because of limited development of the underlying conceptual knowledge required to solve problems, in addition to difficulties in understanding the English-language statement of problems. A study of mathematics reasoning involving bilingual students found that competence in the first language was an important influence on a student’s ability to reason mathematically in a second language (Dave 1983). Researchers have cautioned against confusing language with other processes that also affect learning, such as culture, SES, ethnic-group membership and classroom interaction (DeAvila 1988; MacCorquodale 1988). Home/Societal Factors Family background and parental influence have an effect on both the mathematics attitudes and achievement of students (Tsai and Walberg 1983). This is true for students of color as well as white students. In her study of African American seventh and eighth graders, Rhone (1989) found parental expectation to be a strong predictor of a student’s mathematics achievement and problem-solving ability. The parents of students of color may differ from those of white students in both the level of support they offer and the kind of help they can provide their children. MacCorquodale (1988) found that Mexican American parents were supportive of mathematics education and had higher expectations for their children’s achievement than did white

106 The Middle School Years

parents, although they were more traditional in their attitudes toward sex roles. However, they lacked the experience and information necessary to actively assist their children’s educational efforts. Previous NAEP results have indicated that home support for and involvement in a student’s learning seems to be correlated with proficiency in various subject areas (Mullis and Jenkins 1988). This relationship was probed by several items on the mathematics and science assessments, which collected data on level of parental education (both science and math assessments), home assistance with science homework and projects (science assessment only), participation in science-related activities (science assessment only), number and kinds of reading material in the home, and television-viewing habits (the last two were questioned in both science and math assessments). Results from NAEP suggest that African American and Latino students from lower SES homes are disadvantaged by differences in parental education levels and access to reading materials in the home (Mullis and Jenkins 1988). Tables 9–11 provide responses to questions probing the home environment for science learning by racial/ethnic group. As can be seen in table 9, close to half the white eighth graders had parents who had graduated from college. (The large percentage of responses in the “unknown” category for the fourth-grade sample invalidates the finding for fourth graders.) White fourth and eighth graders were the most likely to be from households that received a newspaper regularly, had an encyclopedia in the home, and had more than twentyfive books in the home. Table 10 shows that white fourth and eighth graders are the least likely to watch six or more hours of television daily, while African American students at the same grade levels are three times as likely as whites to watch that amount of television on a daily basis. Interestingly, African American fourth and eighth graders were much more likely to respond that they had daily help with homework. Table 11 shows white students to be least likely and Latino students most likely to report never discussing studies at home. School-Related Factors The opportunity that students have to study science and mathematics, the types of science and mathematics experiences and instruction they receive in the classroom, and teacher attitudes and expectations provide the school context for the learning of science and mathematics. This context or environment affects how well students learn mathematics and science. The following tables show how some school-related experiences are distributed among the different racial/ethnic groups. The data given in table 12 show the relationship between frequency of science instruction in grade four and science proficiency. In general, fourth graders who received science instruction at least several times a week had a higher proficiency than those who received science instruction less frequently. The data also show that white students were most likely to report receiving science instruction almost every day and least likely to report never having received science instruction. Interestingly, Asian/Pacific Islanders were the least likely to receive science instruction almost every day and the second most likely to respond that they have never had science instruction, yet their proficiency levels are higher than those of any other minority group.

table 9 Student Background/Home Environment Grade 4

White

African American

Hispanic

American Indian

White

African American

Hispanic

American Indiana

5.0 15.7 9.5 35.9 33.9

4.3 17.5 6.3 38.9 33.1

7.7 15.4 7.3 27.6 42.0

5.8 15.9 6.7 30.9 40.8

7.6 25.0 19.1 42.9 5.4

9.2 26.3 18.4 35.0 11.2

18.6 24.0 17.8 21.9 17.8

8.2 29.4 23.1 28.4 10.9

71.9 19.9 8.3 0.7

63.6 27.8 8.6 0.8

61.4 27.9 10.7 0.8

65.1 23.9 11.0 0.0

79.5 18.6 1.9 0.1

68.6 29.3 2.1 0.4

61.8 34.0 4.2 0.4

70.5 25.9 3.7 0.0

70.1 22.8 7.1 0.7

65.1 29.3 5.6 1.2

56.5 35.3 8.2 0.4

59.1 30.5 10.4 0.0

81.6 16.7 1.8 0.1

76.5 20.7 2.7 0.3

65.7 30.9 3.4 0.6

69.1 24.8 6.1 0.0

91.3 2.7 6.0 0.8

75.5 12.6 11.9 0.8

76.1 12.0 11.8 0.7

82.3 12.6 5.0 0.0

93.3 3.3 3.4 0.0

86.4 7.9 5.6 0.6

81.5 11.9 6.7 0.7

87.4 5.9 6.7 0.0

Parents’ educational level Less than high school Graduate high school Some postsecondary Graduate college Unknown Does family get newspaper regularly? Yes No I don’t know Missing Is there an encyclopedia at home? Yes No I don’t know Missing Are there more than 25 books in your home? Yes No I don’t know Missing Source: NAEP. aInterpret

data in this column with caution; error cannot be estimated accurately since coefficient of variation of estimated number exceeds 20%.

Influences on Minority Participation in Mathematics, Science, and Engineering 107

Question

Grade 8

table 10 Study Habits Grade 4

Question

Grade 8

White

African American

Hispanic

American Indian

White

African American

Hispanic

American Indiana

1.5 17.4 20.7 17.5 14.2 8.9 19.8

2.2 12.4 11.6 11.5 8.2 7.4 46.8

2.1 18.0 13.8 15.1 11.0 7.5 32.4

2.0 14.7 12.5 14.8 14.0 9.1 32.9

1.7 13.7 24.2 23.8 17.1 8.9 10.5

1.1 6.9 11.9 14.9 20.0 14.1 31.2

2.0 12.6 21.3 19.6 16.5 11.4 16.6

3.9 11.3 19.6 27.1 15.0 11.0 12.1

29.8 3.8 33.3 24.4 14.8 — —

15.3 5.7 35.6 20.7 22.7 — —

14.5 7.3 35.3 23.6 19.2 — —

21.4 5.2 34.4 22.3 16.7 — —

5.4 7.5 20.6 41.6 — 18.1 6.9

6.5 6.0 19.1 35.1 — 22.8 10.5

7.1 8.1 17.7 38.1 — 21.3 7.7

9.2 10.5 24.5 29.4 22.4 — 4.0

29.8 22.2 8.5 29.4 10.2

44.6 16.6 2.8 31.5 4.4

34.4 22.0 5.3 31.4 6.9

34.7 15.3 4.3 36.5 9.1

16.7 25.7 16.1 36.6 4.8

27.1 25.2 9.2 34.2 4.3

19.3 24.1 10.5 41.4 4.8

25.2 18.0 15.3 33.5 8.0

How much TV do you usually watch each day? None 1 hour 2 hours 3 hours 4 hours 5 hours 6+ hours How much time each day is spent on homework?

108

Have none Don’t do ½ hour 1 hour 1+ hours 2 hours 2+ hours How often does someone at home help with homework? Daily Weekly Monthly Never Have none Source: NAEP. aInterpret

with caution; error cannot be estimated accurately since coefficient of variation of estimated number exceeds 20%.

table 11 Student Responses to the Question: “How Often Do You Discuss Studies at Home?” (1992 Mathematics Assessment) Nine-Year-Olds 1–2 times/week

Daily

Thirteen-Year-Olds

1–2 times/month

1–2 times/week

Daily

Never

1–2 times/month

Never

109

Ethnicity

Number

%

Number

%

Number

%

Number

%

Number

%

Number

%

Number

%

Number

%

White Black Hispanic

4,736 1,434 ,674

53 57 47

2,009 ,387 ,297

23 15 21

490 133 103

5 5 7

1,681 580 365

19 23 25

3,155 852 452

45 41 32

2,295 ,558 ,387

33 27 38

761 166 170

11 8 12

761 496 388

11 24 28

Source: NAEP.

110 The Middle School Years

table 12 Fourth-Grade Students’ Reports on Frequency of Science Instruction in School Almost Every Day

Several Times a Week

About Once a Week

Less Than Once a Week

Never

% students

Average proficiency

% students

Average proficiency

% students

Average proficiency

Grade 4

51

235

21

236

14

230

8

227

6

217

Male Female White Black Hispanic Asian/Pacific Islander American Indian

51 51 54 46 44 39 51

237 234 243 209 216 240 228

22 20 22 20 20 24 19

237 235 246 207 213 230 229

13 15 12 17 18 21 13

232 228 242 203 211 230 233

8 8 8 10 9 8 10

226 227 238 201 205 232 216

6 5 5 7 10 9 6

218 216 230 192 203 223 201

% students

Average proficiency

% students

Average proficiency

Source: The 1990 Science Report Card: NAEP’s Assessment of Fourth, Eighth and Twelfth Grades. Prepared by Educational Testing Service under contract with the National Center for Educational Statistics, March 1992.

Influences on Minority Participation in Mathematics, Science, and Engineering 111

Table 13 shows racial/ethnic differences in the types of science experiences received in the classroom by eighth graders. Here again it seems as though white students are most likely to have had science in school every day or several times a week and are least likely never to have done between five and six types of science experiments. Tables 14 and 15 show calculator and computer use by nine- and thirteen-yearolds. Of nine-year-olds, Hispanics were least likely and African Americans most likely to have a calculator to do their math schoolwork, but at the thirteen-year-old level, whites were most likely to have a calculator. When asked how often they used a computer for schoolwork, although African American nine-year-olds were most (and white nine-yeartable 13 Science Experiences of Students in Grade 8 Question

White (%)

African American (%)

Hispanic (%)

86.2 9.2 0.9 0.5 3.2

82.0 8.7 3.5 0.8 4.9

78.8 11.5 2.5 0.6 6.6

19.7 61.0 17.1 2.2

21.9 62.8 11.4 3.9

19.7 63.4 11.7 5.3

59.6 40.4

56.2 43.8

55.3 44.7

41.9 39.6 18.5

36.3a 35.1a 28.5a

41.2 33.1 25.7

5.2 19.6 37.0 38.2

6.0 32.1 39.5 22.3

7.1 25.7 39.5 27.7

How often do you have science in school? Every day Several times a week Once a week Less than once a week Never How much time do you spend doing science homework each week? None ½–1 hour 2/2+ hours No science Do you ever do science projects in school that take a week or more? Yes No When you study science, how often do you do science experiments? One or more times a week Less than once a week Never Number of types of science experiments? None 1–2 3–4 5–6 Source: NAEP 1990 Science Assessment. aInterpret with caution; rate of nonresponse is high and error cannot be estimated accurately since coefficient of variation

of estimated number of students exceeds 20%.

112 The Middle School Years table 14 Student Responses to the Question: “Do You Have a Calculator to Do Math Schoolwork?” (1992 Mathematics Assessment) Nine-year-olds Yes

Thirteen-year-olds No

Yes

No

Ethnicity

Number

%

Number

%

Number

%

Number

%

White Black Hispanic

3,696 1,160 549

42 47 38

5,130 1,316 880

58 53 62

6,527 1,575 1,048

81 72 70

1,496 624 443

19 28 30

Source: NAEP.

olds least) likely to report daily use of a computer, at the thirteen-year-old level, students from all three groups showed similar rates of daily computer use. White students were least likely to report that they had never used a computer for schoolwork. Teachers’ beliefs about ethnic differences in mathematics and science performance affect their interaction with students. The supportive and nonsupportive behavior that may result from these beliefs thus affects their students’ mathematics and science achievement. Teacher expectations of student achievement potential are lower for students of color (Olstad et al. 1981). Gross (1988) found that each time they enter a new math class, high-achieving African American students must prove themselves to the teacher. Beane (1985) has suggested that teachers tend to believe that students of color are low achievers and their white counterparts are high achievers even when performance is the same. Hall and colleagues (1986) found that teachers in desegregated junior high school science classes rated the ability of white students higher than that of African American students with similar achievement scores. In the same study, teachers also expressed the belief that African American girls made the greatest effort and their male counterparts the least effort. African American students, especially women, are negatively influenced when teachers communicate an expectation of failure (Riggs 1988). Some research has identified school setting as well as instructional approaches that enhance mathematics and science learning of underrepresented students of color. Attitudes held by students of color toward mathematics and performance on reasoning tasks were highest in school settings where teachers showed positive attitudes toward students, felt comfortable with the mathematics curriculum, and had positive interactions with students during mathematics instruction (Pulos, Stage, and Karplus 1982). Schools that were successful in raising the mathematics achievement of African American students on normative and criterion-referenced tests were characterized by a safe and orderly environment, a clear school mission, high expectations for students, the opportunity to learn, sufficient time on task, and frequent monitoring of student progress (Engman 1986). As shown by research on American Indians (Green, Brown, and Long 1978), Latinos (Valverde 1984), and African Americans (Beane 1985), the mathematicseducation program is customarily taught in a manner inappropriate to the needs of people of color, who are underrepresented in mathematics and science. Lockheed

Nine-year-olds 1–2 times/month

1–2 times/week

Daily

Thirteen-year-olds 1–2 times/week

Daily

Never

1–2 times/month

Never

Ethnicity

Number

%

Number

%

Number

%

Number

%

Number

%

Number

%

Number

%

Number

%

White Black Hispanic

428 306 127

5 12 9

1,554 ,542 ,314

17 21 22

851 147 60

10 6 4

6,082 1,537 ,943

68 61 65

563 130 96

7 7 8

1,207 1,267 1,184

16 14 15

1,606 ,266 ,153

21 14 12

4,243 1,191 ,819

56 64 65

Source: NAEP. Note: These figures do not include Asian/Pacific Island, American Indian, or students of other ethnicities.

Influences on Minority Participation in Mathematics, Science, and Engineering 113

table 15 Student Responses to the Question: “How Often Do You Use a Computer for Schoolwork?” (1992 Mathematics Assessment)

114 The Middle School Years

and Gorman (1987) have suggested that teaching strategies and curricula be adjusted to the cultural milieu of students within the classroom. Cohen, Intili, and DeAvila (1982) studied the relationship of cooperative interaction on science and measurement tasks to learning in a hands-on experimental program for bilingual elementary school classrooms. This study of the impact of social status in the classroom on learning showed a clear relationship between status characteristics (social rankings in which it is usually believed that it is better to be in the high than the low state) and peer interaction, with higher status children interacting more. A higher degree of task-related peer interaction resulted in higher achievement. While few studies have examined the relationship of ethnic background to learning styles, there is evidence that middle school–aged African American and Latino students experience positive effects from hands-on inquiry activities (Cohen and DeAvila 1983). These researchers also reported higher student achievement for a bilingual, mostly Hispanic, group of students in a cooperative-structure inquiry program, compared to control classes with a competitive structure. Another approach that has been cited as successful in the science instruction of students of color has been the use of activity-based science programs in the classroom. Reynolds (1991) studied the effects of an experiment-based physical science program on the science content and process skills of a group of African American, Latino, and white urban fourth through eighth graders. Results showed that the program had a significant influence on science process skills but not on content skills. An additional finding was that while Latino and white students benefited from the program, African American students did not. In general, girls did better than boys, net of other factors such as race or grade level. A partial explanation of the low performance of students of color in mathematics and science has been their underrepresentation in advanced high school mathematics courses. This can be attributed, at least in part, to the cumulative effect of lower performance that begins in elementary school and is not addressed by early interventions. By the end of the elementary school period, just as critical decisions are being made regarding class placement for high school mathematics, as many as one third to one half of African American and Latino students have fallen far behind, thus restricting their eligibility to take future higher level mathematics courses (Gross 1988). Research has also shown that schools for early adolescents face major structural– functional issues in organizing instructional staffing, scheduling, and grouping to meet the new realities that emerge in the middle years. One major feature is the greater diversity of students’ current academic accomplishments in the middle grades, which is usually met with some form of tracking that places students in different classes based on their recent grades or test scores. But tracking is widely believed to have frequent negative impacts on the motivation and learning environments of the lowest level classes. To be sure, student diversity is also found at earlier grades and requires some modifications of instructional grouping and scheduling. But early adolescents of the same age reflect a much wider range of individual differences than younger children due to the greater disparity among students in academic achievement after several years of schooling and significant variations in the timing of physical, social, and

Influences on Minority Participation in Mathematics, Science, and Engineering 115

cognitive changes at this stage of life (Scales 1991). Student diversity in the elementary grades usually does not require separate classes for different levels of student performance (except for special-education placements), being handled in each teacher’s mixed classes by within-class ability grouping for reading or math instruction, which research finds can have positive effects (Slavin 1987, 1990). At the middle grades, between-class grouping in some or all subjects, based on students’ recent grades and test scores, is the major way instruction is organized to address student diversity (Braddock 1990). Between-class grouping or “tracking” has been severely criticized because it can restrict a student’s opportunity to learn when lower level classes have different course content (such as remedial math rather than algebra), poorer resources (such as less experienced teachers), or weaker motivational environments (such as lower teacher and student expectations) (Oakes 1991, 1992). Data in table 16 on eighth graders show that Asians and whites are most likely to be placed in highability groups and least likely to be in low-ability groups in mathematics and science classes than their African American, Latino, and American Indian counterparts. Although at the middle school level few opportunities exist for students to determine their own participation in mathematics and science courses since these subjects are usually part of the curriculum being taught to everyone (Lockheed et al. 1985), attitudes that influence course taking in high school may already be present. How much of these attitudes can be explained by placement in different-ability groups is not clear, but as early as junior high some African American and Latino students express the belief that science is optional without being aware of the effect of this belief on their educational and occupational choices (MacCorquodale 1980). In attempting to identify the determinants of middle school students’ intentions to enroll in a high school science course, Crawley and Coe (1990) found that the relative contributions of attitude and social pressures to the prediction of intention varied according to gender, race/ethnicity, general ability, and science ability. Students of color in the study valued the opportunity to do “fun experiments,” this being the major determinant for them. (The study did not report race by gender interactions.)

table 16 Eighth Graders’ Ability Placement in Science and Mathematics, by Ethnicity (%) Mathematics ability group

Science ability group

Low

Middle

High

Low

Middle

High

Nation

24

45

31

22

49

29

White Black Hispanic Asian American Indian

20 38 32 13 36

46 43 47 34 51

34 19 21 53 13

18 34 30 14 37

51 43 51 45 43

31 23 19 41 20

Source: National Educational Longitudinal Study of 1988 (NELS:88). National Center for Educational Statistics, U.S. Department of Education. Tabulations derived from base-year, public school sample.

116 The Middle School Years

Summary of Research Findings Asian/Pacific Islanders and whites outperform African American and Latino students in standardized tests of both mathematics and science. The achievement gap widens with age, although recent data from the NAEP mathematics assessment show that this trend may be changing. Difference in performance by racial/ethnic group may begin as early as the third grade. Within recent years both African American and Latino students have experienced gains in achievement, although gaps still remain substantial, and the most recent NAEP mathematics assessment (1992) shows white students’ gains to be larger overall than those of African American and Latino students. This assessment also contains data suggesting that instead of widening as students progress through school, as has been the trend, the gender gap in mathematics performance favoring African American and white males closes at the eighth grade before widening again from the eighth through twelfth grades. Most notably for Latino students, females outperform males at all grade levels. Cognitive orientations or styles of underrepresented students of color may have inhibited performance by these groups in mathematics and science. African American and Latino students are more likely to be field dependent and to have an external locus of control than are their white counterparts. Level of English proficiency rather than bilingualism seems to be the determining factor affecting mathematics achievement of bilingual students. African American and Latino parents are more highly supportive of and have higher expectations for their children’s learning of mathematics than do white parents, and parental expectation is a strong predictor of African American students’ mathematics achievement. African American and Latino students, however, are more likely than are white students to have parents with lower levels of educational attainment, to come from homes where fewer educational materials are available, and to watch more television. African American students are the most likely of all groups to report discussing studies at home on a daily basis. Underrepresented students of color have less exposure to important science- and mathematics-related experiences and instruction in the classroom than do their white counterparts. In addition, teachers tend to have lower expectations for, and are less supportive of, the science and mathematics learning of underrepresented students of color. Cooperative learning and inquiry-based learning are effective instructional approaches in mathematics and science for students of color. The underrepresentation of students of color in advanced high school mathematics courses has its roots in their lower achievement in elementary school and subsequent placement in lower ability tracks during middle school.

Career Interests and Aspirations Enduring differences still exist between the major social groups in terms of aspirations and careers in different fields of work, especially race and gender differences. Research has shown that aspirations and interests are useful in predicting field of

Influences on Minority Participation in Mathematics, Science, and Engineering 117

work actually entered (Nafziger et al. 1974; Lucy 1976; Campbell 1971). This research also provides insights into how the process occurs: people who are interested in and aspire to different types of work are also likely to develop different sorts of skills, acquire different types of schooling or training, seek out different types of occupational experiences and information, and actively look for different types of jobs when they enter the labor force (Holland 1973). Gottfredson (1981) has proposed a developmental theory of occupational aspirations in which she describes the processes that condition the images and dimensions of people’s occupational preferences at different stages of development, as well as how priorities are used in reaching a goal among conflicting goals (between sextype, prestige, and field of work) in light of people’s perceptions of their opportunities for implementing their choices. Gottfredson (1978) has also conducted research on elementary and secondary school students’ aspirations for different occupational types of work indicating that racial differences occur well before college enrollment and major-field selection. This research, based on the National Assessment of Career and Occupational Development, shows that students have similar occupational expectations and values in the elementary and middle school years, which subsequently diverge toward the end of high school to match traditional race and sex stereotypes, and continue to diverge after initial employment. Divergence may emerge earlier along gender lines than along racial/ethnic lines, however. Specifically with regard to aspirations for scientific careers, Gottfredson found that among thirteen-year-olds white males and African American males revealed a similar interest in becoming “natural scientists,” while white and African American females’ occupational aspirations for scientific careers were unlike each other as well as those of their male counterparts. However, among seventeen-year-olds white and African American males revealed quite dissimilar interests in becoming “natural scientists,” while white and African American females’ occupational aspirations for scientific careers were more similar but still unlike those of their male counterparts. The implications suggest that major race and gender differences influencing choice of math and science majors and selection of careers in these areas will remain even after existing disadvantages in social-class background and educational attainment are removed unless there are changes in the occupational socialization processes that differentially channel females and students of color into traditional race or gender-typed occupations. When asked what kind of work they expected to do at age thirty, eighth graders of different racial/ethnic groups surveyed as part of the National Educational Longitudinal Study (NELS) of 1988 had different aspirations for careers in science/engineering. Their responses are shown in table 17. Asians (11.3%) were by far most likely to opt for science/engineering, followed by American Indians (7%) and whites (6.7%). African Americans (4.2%) and Latinos (5.1%) were least likely to aspire to be scientists or engineers. Career preferences can be established as early as junior high school (Strauss and Rainwater 1962) and, as noted earlier, a substantial amount of career-related stereotyping has already taken place by the middle school level. Research has suggested that both female and African American students are socialized early in their

118 The Middle School Years table 17 Eighth-Graders’ Aspirations for Employment in Science/Mathematics Careers, by Ethnicity (%) Career field at age 30

Nation White Black Hispanic Asian American Indian

Science/math career

Other career

6

94

6 4 5 11 6

94 96 95 89 94

Source: National Educational Longitudinal Study of 1988 (NELS:88). National Center for Education Statistics, U.S. Department of Education. Tabulations derived from base-year, public school sample.

educational development to be more “affective” and less “analytical” and “quantitative” than their white counterparts in their career aspirations (Steel 1978; Young 1981). At the middle school level, influences on the choice of science and sciencerelated careers include: sex and race/ethnic typing of these careers; participation in mathematics and science hobbies and clubs; informal interactions with parents and older siblings around mathematics and science activities; and exposure to scientists and information about science careers. Krist (1993) found that for the high-ability African American women in her study, middle school was a critical period for making decisions about the pursuit of science- and mathematics-related careers. Sex and Racial/Ethnic Typing of Careers Thomas (1982) has reviewed a variety of socialization or perceptual factors related to why females and people of color may not actively pursue careers in scientific and technical fields. Females and students of color have been found to internalize at an early age gender and ethnic stereotypes and traditional career roles and expectations. These influences affect students’ self-concept as potential “doers” of science and, ultimately, their vocational preferences. For example, students of color and females may perceive themselves to be better suited for jobs in teaching, clerical, and other social service–oriented careers and less suited for investigative occupations involving science and mathematics (Gottfredson 1978). In her study of African American eighth graders, Jacobowitz (1983) found self-concept to be a better predictor of science-career preference than any variable other than sex, with boys showing a significantly higher science self-concept than girls. Traditional role definitions may be reinforced by the family (Persell 1977; Rosenbaum 1976) or by school officials as they channel females and students of color into nonacademic curricular programs and away from rigorous science and mathematics courses in middle and high schools (Oakes 1991).

Influences on Minority Participation in Mathematics, Science, and Engineering 119

Exposure to Informal Science Activities In her study of influences affecting the interest of high school students in mathematics and science majors and careers, Thomas (1986) found that early interest in science hobbies and participation in mathematics clubs in high school were important influences. The study found that participation in mathematics clubs was more strongly related to interest in high school mathematics for African Americans than for whites, although the former were less likely to participate in these clubs (Thomas 1984). Anderson and Pearson (1988) found that 80 percent of the high-ability African American students who did not persist in their pursuit of a science career reported not having participated in an investigative science project; a majority of these students either did not have access to or chose not to participate in extracurricular science programs. Other research has emphasized the value of early participation in extracurricular activities in science and mathematics in providing students with support, encouragement, and career counseling (Fox 1981; Stallings and Robertson 1979). Table 18 shows that in 1988 African American, Latino, and American Indian eighth graders were more likely to have participated in a science or math club than either their white or Asian counterparts. This higher participation rate might reflect the rapid growth in intervention programs in mathematics and science for middle school–aged students of color during the eighties. Opportunities to Learn About Mathematics- and Science-Related Careers The fact that there are so few scientists of color in society limits the opportunity that students of color have to learn about careers in science fields. An important finding of a study of factors affecting the attitudes of African Americans toward the pursuit of science and science-related careers was that personal acquaintance with a scientist was a major contributing factor to pursuing a career in science (Hill, Pettus, and Hedin 1990). The authors felt that one of the reasons for this was that those personally ac-

table 18 Eighth-Graders’ Participation in School-Based Science/Mathematics Clubs, by Ethnicity (%)

Nation White Black Hispanic Asian American Indian

Science club

Mathematics club

5

6

4 10 7 6 8

4 11 7 10 11

Source: National Educational Longitudinal Study of 1988 (NELS:88). National Center for Education Statistics, U.S. Department of Education. Tabulations derived from base-year, public school sample.

120 The Middle School Years

quainted with scientists were more likely to be knowledgeable about potential careers and salary levels of scientists. Schools play a major role in initiating the processes that direct individuals toward different occupational settings in the sense that they provide a major socialization and preparatory setting for student aspirations in specific fields of work, especially mathematics and science. Teacher encouragement and support are important influences on students’ attitudes toward academic subjects; however, some researchers have suggested that many African American (and other minority) students are actively discouraged from pursuing their interest in mathematics and science (Malcom 1983; Thomas 1984). Formal vocational guidance programs are offered by many schools to introduce students to a range of career opportunities, including the prerequisites for entering these careers, and some method of assessing their personal aptitudes and interests for these careers. Although these programs are often taken by students during the junior or senior year of high school, one study has suggested that these courses come too late in a student’s career development to be helpful (Okey, Snyder, and Hackett 1993). This same study of a vocational guidance program designed to expand the career aspirations of eighth-grade girls, minority students, and handicapped students to include careers involving mathematics and science describes the use of experiential learning to increase confidence among students in mathematics and science skills. The study concluded that the course was quite successful in increasing students’ selfconfidence in their ability to aspire to careers in mathematics and science; the researchers emphasized the need for early intervention in the career development of students. Summary of Research Findings Research on the process by which individuals choose careers suggests that people who are interested in and aspire to different types of work are also likely to develop different types of skills, acquire different types of schooling/training, seek out different types of occupational experiences and information, and actively look for different types of jobs upon entering the labor force. Major differences—particularly those of race and gender—exist among the individual social groups in terms of aspirations and careers in various fields of work. Students have similar occupational expectations and values in the elementary and middle school years that diverge toward the end of high school and become more like the traditional career aspirations for each student’s racial/ethnic and gender group. Career preference can be established as early as junior high. Self-concept is a strong predictor of science-career preference among African American eighth graders. Gender and racial stereotypes regarding careers in mathematics and science affect students’ self-concept as potential “doers” of mathematics and science. Early interest in science hobbies and participation in high school mathematics clubs and extracurricular science activities are important influences on the interest of African American students in mathematics and science majors and careers. For African American middle school students, personal acquaintance with a scientist was a major factor contributing to student interest in pursuing a career in

Influences on Minority Participation in Mathematics, Science, and Engineering 121

science. Experiential learning in vocational guidance programs to expand the career aspirations of middle school students to include mathematics and science is an effective strategy for increasing confidence in mathematics and science skills.

Efforts at the Middle School Level to Address the Underrepresentation of Students of Color in Mathematics and Science During the last twenty years, the realization that people of color have not been represented in scientific and engineering fields has resulted in efforts to address this problem. The most effective and focused efforts have taken place outside the traditional educational system rather than within the school system. Known collectively as intervention programs, these efforts encompass a variety of formats, structures, and approaches. The following section describes mathematics and science intervention programs for middle school students of color. The Nature of Intervention Programs in Mathematics and Science Intervention programs in mathematics and science to increase the participation of students of color in these fields emerged from the civil rights movement of the sixties and seventies. These programs had their origins in the realization that women and some racial/ethnic groups—African Americans, Latinos, and American Indians— were severely underrepresented in mathematics- and science-related careers. At first the programs operated at the local level, addressed local needs, and were funded by local sources. With time, however, they began to attract national attention and, as a result, federal and foundation funding increased (Malcom et al. 1984). Since intervention programs arose out of the recognition that the formal education system had failed to address the problem of the underrepresentation of certain groups in mathematics and science, it is logical that they should utilize approaches that are different from those used by the traditional system of education. In addition to reinforcing the lessons of the classroom, intervention approaches address the lack of participation of students of color (and women) in mathematics- and sciencerelated careers by addressing the barriers known to exist for these groups. These barriers include negative attitudes toward mathematics and science, lack of information regarding careers in these fields, low performance levels in these subjects, and failure to participate in higher level mathematics and science courses in high school (Clewell 1987). Thus, intervention programs typically provide activities to affect attitudes toward mathematics and science, suggest strategies to improve performance and achievement in these subjects, and promote career awareness of these fields in order to help students attain their goals. The effectiveness of intervention programs in mathematics, science, and computer science was acknowledged in 1983 when the National Science Board Commission on Precollege Education in Mathematics, Science and Technology called for a wide range of activities to supplement formal education in mathematics and science (National Science Board 1983). More recently,

122 The Middle School Years

a report from the Office of Technology Assessment (U.S. Congress 1988) cited the effectiveness of these programs in encouraging women and people of color to consider careers in science and engineering. Intervention in the Middle School Years At first mathematics and science intervention programs targeted undergraduate and high school students. Subsequently researchers and practitioners alike came to realize that interventions that aimed at widening the pool of potential mathematics or science majors must begin before high school (Berryman 1983). This realization resulted in a growing focus on intervention efforts directed at middle and junior high school students (Clewell 1988). The most comprehensive study of intervention programs in mathematics and science targeting middle school students of color was funded by the Ford Foundation in 1986 and was conducted in two phases. The first phase resulted in a report (Clewell, Thorpe, and Anderson 1987) that identified and described 163 mathematics and science intervention programs serving females and students of color in grades four through eight. Tables 19–24 give the characteristics of the programs in terms of subject-area focus, grade levels and population served, strategies and approaches utilized by the programs, and geographic distribution. The first phase of the study found that the programs varied as to format, size, contact time with students, and activities offered, with the most predominant format being that of the in-school program (62% of all programs). Many of these in-school programs were cooperative ventures involving the school and a university or industry. The in-school programs usually took place during the school day on school premises. More than half (57%) of the in-school programs provided services beyond the school day, such as after-school, Saturday and/or summer programs (Clewell, Thorpe, and Anderson 1987). It should be emphasized that these programs, although conducted on school premises during the school day, provided supplemental services that were not integrated into the regular school curriculum. The programs served a small proportion of students in the schools and were generally perceived as entities operating outside the school system. The first phase of the study also revealed that there were gaps in service delivery even though there were many more of these programs than the literature or anecdotal information led the researchers to expect. The number of programs appeared

table 19 Target Population Target Minorities Females Minorities and females

N (programs)

Percent

54 21 88

33 13 54

Source: Clewell et al. 1987. Intervention Programs in Math, Science, and Computer Science for Minority and Female Students in Grades Four through Eight. Princeton, NJ: Educational Testing Service.

Influences on Minority Participation in Mathematics, Science, and Engineering 123 table 20 Subject-Area Focus Subject

N (programs)

Percent

29 17 4 39 5 64

18 11 3 24 3 40

Math Science Computer science Math/science Science/computer science Math/science/computer science

Source: Clewell et al. 1987. Intervention Programs in Math, Science, and Computer Science for Minority and Female Students in Grades Four through Eight. Princeton, NJ: Educational Testing Service.

low in relation to the population of minority female students within this age range. The preponderance of programs focused at or above the sixth grade, with many of them extending into high school, while only a third served students in the fourth or fifth grades. There was a dearth of intervention programs targeting students in the Southeast. Since this area contains a large African American population, it is possible that African American middle school students in the Southeast are being underserved. The same situation was identified for American Indians; several of the states with a high concentration of American Indians seemed to have no intervention programs for middle school students (Clewell, Thorpe, and Anderson 1987). The second phase of the study brought together what is known about these intervention programs by identifying the range of effective strategies utilized by the programs and linking them to the theory and empirical research that undergird them (Clewell, Anderson, and Thorpe 1992). This study divided the programs into two groups: those that focus on increasing the mathematics and science skills of participants (skills-development programs) and those that focus on increasing the participation of females and minorities in mathematics and science careers (career choice programs). Skills-development type programs (a) focus on increasing

table 21 Population Served Population Females Minorities Blacks Mexican Americans Puerto Ricans Other Hispanics Native Americans Asian Americans Whites

N (programs)

Percent

109 143 135 67 67 46 57 34 95

67 88 83 41 41 28 35 21 58

Source: Clewell et al. 1987. Intervention Programs in Math, Science, and Computer Science for Minority and Female Students in Grades Four through Eight. Princeton, NJ: Educational Testing Service.

124 The Middle School Years table 22 Grade Levels Served Grades 4 5 6 7 8

N (programs)

Percent

55 64 88 127 132

34 39 54 78 81

Source: Clewell et al. 1987. Intervention Programs in Math, Science, and Computer Science for Minority and Female Students in Grades Four through Eight. Princeton, NJ: Educational Testing Service.

math and science skills of participants; (b) serve younger children; (c) focus on improving performance; and (d) measure success by noting any improvement of participants’ performance. Career choice programs (a) focus on increasing the participation of females and minorities in math and science careers; (b) target a population that includes junior high school and older students; (c) focus on career awareness, enrollment in higher-level courses, and performance improvement; and (d) evaluate students over a period of several years to determine changes in behavior, in addition to measuring changes in attitude and performance (Clewell, Anderson, and Thorpe 1992). The second phase of the Ford Foundation study identified characteristics of effective intervention programs in mathematics and science for middle school minority and female students (Clewell, Anderson, and Thorpe 1992). The study found that the best programs developed strategies to address barriers hindering participation of the target group in mathematics and science identified in the research. The

table 23 Program Activities Activity Role models Direct instruction Counseling Field trips/tours Guest speakers Hands-on experiences Special projects Contests/science fairs Study groups/clubs Tutoring Test preparation Job shadowing

N (programs)

Percent

88 108 96 77 88 137 54 46 38 36 29 11

54 66 59 47 54 84 33 28 23 22 18 7

Source: Clewell et al. 1987. Intervention Programs in Math, Science, and Computer Science for Minority and Female Students in Grades Four through Eight. Princeton, NJ: Educational Testing Service.

Influences on Minority Participation in Mathematics, Science, and Engineering 125 table 24 Geographic Distribution of Programs West Arizona California Colorado Hawaii Idaho Montana New Mexico Oklahoma Oregon Texas

Central 3 21 6 3 1 1 3 4 1 6

Illinois Indiana Iowa Kansas Michigan Minnesota Nebraska Ohio Wisconsin

Northeast 9 5 3 1 7 4 1 7 3

Connecticut Delaware District of Columbia Maryland Massachusetts New Jersey New York Pennsylvania Rhode Island

Southeast 4 1 9 4 3 5 12 6 1

Alabama Arkansas Florida Georgia Louisiana North Carolina Virginia West Virginia Puerto Rico

1 1 2 12 1 3 7 1 1

Source: Clewell et al. 1987. Intervention Programs in Math, Science, and Computer Science for Minority and Female Students in Grades Four through Eight. Princeton, NJ: Educational Testing Service.

findings of research were also reflected in the specific approaches utilized by these programs. Effective intervention programs at the middle school level • offered a mix of services and activities, including academically oriented activities • emphasized enrichment rather than remediation • were innovative and creative in designing approaches to address the problem of underrepresentation • utilized multiple approaches and strategies • used inquiry/discovery approaches in teaching as well as activity-based, hands-on activities • incorporated some of the characteristics of cooperative goal structures, such as lack of competitiveness, a high degree of interaction among students, and group responsibility for goal achievement • taught problem-solving and/or higher-order thinking skills • used instructional techniques and strategies appropriate to their target populations • worked with groups of students with mixed levels of ability • required a high level of student involvement and activity as a result of being student-centered • trained teachers and other staff to introduce them to program objectives as well as program strategies and approaches • employed instructors who had high expectations of students, gave them feedback and encouragement, had a thorough knowledge of the subject matter, knew the populations they were teaching, and had the ability to accommodate instruction to their students’ cognitive and learning styles • maintained a high level of parental involvement and strove to attract, involve, and work with parents of participants Some of the strategies used by programs to encourage positive attitudes toward mathematics and science and increase participants’ awareness of opportunities for

126 The Middle School Years

careers in these fields included exposure to role models, information about careers in mathematics and science, opportunities for out-of-school science and math activities, and providing a supportive environment within the confines of the program, both in school and at home. Instructional techniques common to all programs were the use of inquiry/discovery approaches and hands-on activities involving the use of manipulatives. There was very little use of the traditional lecture mode; when used, it was combined with the discovery/inquiry approach. All programs utilized some of the characteristics of cooperative goal structures and taught problem solving under one guise or another. Intervention programs are different from traditional educational programs in that they are willing to develop creative, innovative approaches to teaching and to take risks. In short, in the absence of generally accepted strategies and designs for effective intervention, they have been resourceful and unconventional in devising solutions to the problem of low minority participation in mathematics and science. Transferring Successful Intervention Approaches to the Classroom The ultimate measure of success of an effective intervention practice is its transfer to a classroom setting. Intervention programs in and of themselves can reach only a small percentage of the target population. Although many successful programs make an effort to disseminate and institutionalize their practices, the effect is often of a local or regional nature. There is a need for more research on effective methods of transferring the lessons learned from intervention programs to the regular classroom setting. The current school-reform movement provides an excellent opportunity for doing so.

Unanswered Questions and Gaps in the Research Although the research reviewed in this chapter answers some questions, in doing so it raises many more questions regarding the factors that affect the participation of underrepresented students of color in mathematics and science. This section identifies some of the gaps in research and discusses some general issues related to the status of research for this target population. In the area of influences on attitudes, the following questions still require further research: 1. How do students’ attitudes toward mathematics and science differ by sex within each racial/ethnic group? Have these differences changed over time? Is the relationship between liking mathematics/science and achievement the same for students of color as it is for white males? 2. How do students’ self-concept and perception of the utility of mathematics and science differ by sex within racial/ethnic groups? 3. Is the use of role models as a strategy for improving students’ attitudes toward mathematics and science more effective if the role models are

Influences on Minority Participation in Mathematics, Science, and Engineering 127

matched by sex and race/ethnicity? Does this effectiveness vary by racial/ ethnic group? By sex within racial/ethnic groups? 4. Do the attitudes of male peers affect those of female middle school students regarding the sex-typing of mathematics and science? How does this effect differ by racial/ethnic group? In the area of influences on achievement performance, the following questions still require further research: 1. What are the trends in gender differences in achievement/performance in mathematics and science for each racial/ethnic group? 2. What is the role of social class or SES in influencing achievement? 3. How does self-concept in mathematics and science differ by sex within racial/ethnic groups? What is its relationship to achievement for these groups? 4. What is the relationship between test anxiety and performance by racial/ ethnic group and by sex within racial/ethnic groups? 5. Is the lower achievement of underrepresented students of color in mathematics and science due to cognitive orientations or styles different from those of whites, or is it due to the fact that teaching approaches in these subjects tend to be geared to the cognitive style of the dominant group in society, namely, white males? 6. How do culture, SES, ethnic-group membership, and classroom experiences interact with language in affecting the learning of mathematics for bilingual students? 7. How much of the positive effect of higher support and expectation levels on the part of African American and Latino parents is diluted by their lower levels of education, experience, and information to actively assist their children’s efforts? What about fewer educational materials or longer hours of television viewing in the home? In the area of career interests and aspirations, the following questions still require further research: 1. What are the factors that affect divergence of occupational expectations and values according to gender and race/ethnicity? Does divergence occur first according to gender or according to race/ethnicity? What is the process whereby divergence occurs?

General Issues There is a need to assess the effect of gender, as distinct from that of race/ethnicity, in terms of how the factors affecting attitudes, performance, and career interests are played out. For example, there is some indication that African American girls and Latinas may have more positive attitudes, higher self-concepts, and/or higher performance levels than do their male counterparts. In what ways are differences gender-

128 The Middle School Years

based and in what ways are they racially/ethnically based? The effect of social class or SES and its interaction with sex and race must also be explored. The characteristics and experiences of high-ability students of color require further research. It will be necessary to test what we know about the processes that lead to white males entering science-related careers to see whether these processes are similar for their female counterparts, as well as for males and females of other racial/ethnic groups. The route that leads to the participation of students of color or females may be different from that of white males. The indication that for underrepresented students of color liking science/mathematics may not be related to higher achievement in these subjects is a case in point. The home environment and how parental education, support, and expectations affect different groups of students is another area that needs further research. Social capital might be more effective than parental support and expectations in channeling students into careers in science and mathematics. The influence and role of the extended family, friends, and the community—including the church—for African American and Latino students must be explored. Research must also be conducted against the background of change that is occurring in society in general, in school reform, in the development and implementation of mathematics and science standards, and in the increasing effectiveness of intervention programs. Data must be gathered and updated frequently to identify changing trends. Studies undertaken by NAEP and the National Science Foundation’s Indicator Series are good examples of this type of effort. Research on the transfer of intervention strategies and approaches to the classroom is a large and very important area that merits greater interest on the part of researchers. It is our feeling that research has reached the stage where more in-depth and disaggregated probing is called for to get at the causes of underparticipation and the solutions to underrepresentation of students of color in mathematics and science. The importance of collecting and reporting data that are disaggregated by race/ ethnicity and by sex within racial/ethnic groups cannot be emphasized enough. Longitudinal studies using databases such as NELS:88 and High School and Beyond have the potential to answer many of the previously unanswered questions about the processes that lead to underrepresentation.

Policy Issues This concluding section raises several issues for discussion relating to state and national policy as well as to school district and school policy. These issues are presented in the form of policy recommendations. In the area of state and national policy, the following recommendations should be implemented: 1. Data collected as part of large state and/or national databases such as NAEP, NELS:88, High School and Beyond should be disaggregated and reported by race/ethnicity and by sex within racial/ethnic categories.

Influences on Minority Participation in Mathematics, Science, and Engineering 129

2. State-certification requirements for teachers of mathematics and science should include competence in the teaching of diverse students and a knowledge of these students’ characteristics. 3. State reform in science and mathematics, such as the Statewide Systemic Initiatives and National Goals efforts, should have as one of its goals the increase in achievement and participation levels in mathematics and science by underrepresented students of color and females. These efforts should incorporate research findings and transfer to classroom practices those strategies found to be effective in intervention programs. In the area of school districts/school policies, the following recommendations should be implemented: 1. School districts/schools should be responsive to research findings on early adolescence in organizing instructional staffing, scheduling, and grouping to meet the challenges that emerge in the middle years, especially with regard to ability-grouping practices. 2. School districts/schools should provide and require teacher attendance at in-service professional development workshops to prepare mathematics and science middle school teachers to teach these subjects effectively to diverse groups of students. 3. School districts/schools should adjust teaching strategies and curricula in mathematics and science to the cultural milieu of the students being taught. 4. School districts/schools should monitor the access of students of color to opportunities to learn mathematics and science. In addition to tracking progress in the area of achievement, they should monitor distribution in high-ability groups, participation in higher level mathematics courses, receipt of frequent science instruction, and participation in meaningful science and mathematics learning activities such as long-term science experiments. 5. School districts/schools should closely monitor the mathematics performance of African American and Latino students in the early elementary school years in order to identify problems in achievement sooner and provide appropriate remediation to assist these students to remain at their appropriate grade levels.

Selected References Anderson, B. T., and W. Pearson, Jr. 1988. Understanding the underrepresentation of Blacks in American science: Why capable Blacks do not persist in their pursuit of scientific careers. Paper presented at the Joint Conference of the Society for the Social Studies of Science (4S) and the European Association for the Study of Science and Technology (EASST). Amsterdam, The Netherlands. Andrews, J. V. 1989. Attitudes and beliefs about mathematics: Do parents, students, teachers, counselors and principals agree? Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.

130 The Middle School Years Anick, C. M., T. P. Carpenter, and C. Smith. 1981. Minorities and mathematics: Results from the National Assessment of Educational Progress. Mathematics Teacher 74(7): 560– 566. Armstrong, J. M. 1981. Achievement and participation of women in mathematics: Results of two national surveys. Journal for Research in Mathematics Education 12: 356–372. Beane, D. B. 1985. Mathematics and science: Critical filters for the future of minority students. Washington, DC: The Mid-Atlantic Center for Race Equity, The American University. Berryman, S. E. 1983. Who will do science? Trends and their causes in minority and female representation among holders of advanced degrees in science and mathematics. New York: Rockefeller Foundation. Braddock, J. H. 1990. Tracking the middle grades: National patterns of grouping for instruction. Phi Delta Kappan 71: 445–449. Brown, G. H., N. L. Rosen, S. T. Hill, and M. A. Olivas. 1980. The condition of education for Hispanic Americans. Washington, DC: National Center for Education Statistics. Brush, L. 1980. Encouraging girls in mathematics: The problem and the solution. Cambridge, MA: Abt Books. Buriel, R. 1975. Cognitive styles among three generations of Mexican American children. Journal of Cross-Cultural Psychology 6(4): 417–429. Campbell, D. 1971. Handbook for the Strong vocational interest blank. Stanford, CA: Stanford University Press. Campbell, P. B. 1986. What’s a nice girl like you doing in a math class? Phi Delta Kappan 67(7): 516–520. Catsambis, S. 1991. Gender, race/ethnicity, and science education in the middle grades. Baltimore, MD: Center for Social Organization of Schools, The Johns Hopkins University. Clewell, B. C. 1987. What works and why: Research and theoretical bases of intervention programs in math and science for minority and female middle school students. In A. B. Champagne and L. E. Hornig, eds., This year in school science, 1987: Students and science learning, 95–133. Washington, DC: American Association for the Advancement of Science. ——— . 1988. Intervention programs in science and mathematics for minority and female students: The growing focus on the middle school years. Paper presented at the Joint Conference of the Society for the Social Studies of Science (4S) and the European Association for the Study of Science and Technology (EASST). Amsterdam, The Netherlands. Clewell, B. C., B. T. Anderson, and M. Thorpe. 1992. Breaking the barriers: Helping female and minority students succeed in mathematics and science. San Francisco: Jossey-Bass. Clewell, B. C., M. Thorpe, and B. T. Anderson. 1987. Intervention programs in math, science, and computer science for minority and female students in grades four through eight. Princeton, NJ: Educational Testing Service. Cohen, E. G., and E. A. DeAvila. 1983. Learning to think in math and science: Improving local education for minority children. Final report to the Walter S. Johnson Foundation. Stanford, CA: Stanford University School of Education. Cohen, E. G., J. Intili, and E. A. DeAvila. 1982. Learning science in bilingual classrooms: Interaction and social status. Stanford, CA: Stanford University Center for Educational Research. Coles, M., and P. Griffen. 1987. Contextual factors in education: Improving science and mathematics education for minorities and women. Madison, WI: Wisconsin Center for Education Research.

Influences on Minority Participation in Mathematics, Science, and Engineering 131 Crawley, F. E., and A. S. Coe. 1990. Determinants of middle school students’ intention to enroll in a high school science course: An application of the theory of reasoned action. Journal of Research in Science Training 27: 461–476. Creswell, J. L., and R. H. Exezidis. 1982. Research brief: Sex and ethnic differences in mathematics achievement of black and Mexican American adolescents. Texas Tech Journal of Education 9: 219–222. Cuevas, G. J. 1984. Mathematics learning in English as a second language. Journal for Research in Mathematics Education 15(2): 134–144. Dave, L. 1983. Bilingualism and mathematical reasoning in English as a second language. Educational Studies in Mathematics 14(4): 325–353. DeAvila, E. A. 1980. Relative language proficiency types: A comparison of prevalence, achievement level, and socioeconomic status. Report submitted to the Rand Corporation. ——— . 1988. Bilingualism, cognitive function, and language minority group membership. In R. R. Cocking and J. P. Mestre, eds., Linguistic and cultural influences on learning mathematics, 101–121. Hillsdale, NJ: Lawrence Erlbaum. DeAvila, E. A., and S. Duncan. 1980. The language minority child: A psychological, linguistic, and social analysis (Report No. 400–65–0051). Washington, DC: National Institute of Education. Dossey, J. A., I. V. S. Mullis, M. M. Lindquist, and D. L. Chambers. 1988. The mathematics report card: Are we measuring up? Trends and achievement based on the 1986 national assessment (Report No. 17–M–01). Princeton, NJ: The Nation’s Report Card, NAEP, Educational Testing Service. Duran, R. P. 1987. Hispanics’ precollege and undergraduate education: Implications for science and engineering studies. In L. S. Dix, ed., Minorities: Their underrepresentation and career differentials in science and engineering, 73–128. Washington, DC: National Academy Press. Eccles, J., S. Lord, and C. Midgley. 1991. What are we doing to early adolescents? The impact of educational contexts on early adolescents. American Journal of Education 99: 521– 542. Eccles-Parsons, J., T. F. Adler, R. Futterman, S. Goff, C. Kaczala, J. L. Meece, and C. Midgley. 1983. Expectancies, values and academic behaviors. In J. T. Spence, ed., Achievement and achievement motives: Psychological and sociological motives, 75–146. New York: W. H. Freeman. Engman, L. R. 1986. School effectiveness characteristics associated with minority student mathematics achievement. Ph.D. diss., George Washington University, 1986. Abstract in Dissertation Abstracts International 47, sec. 3: 813A. Erickson, D. K. 1987. A review of research on the effect of mathematics teachers’ classroom behavior on girls’ and boys’ learning, attitudes, and participation in mathematics. Paper presented at the American Education Research Association Special-Interest Group on Research on Women in Education Conference, Portland, OR. Fennema, E., and J. A. Sherman. 1978. Sex-related differences in mathematics achievement and related factors: A further study. Journal for Research in Mathematics Education 9(3): 189–203. Fox, L. H. 1981. The problem of women and mathematics. New York: Ford Foundation. Garcia, J., N. R. Harrison, and J. L Torres. 1990. The portrayal of females and minorities in selected elementary mathematics series. School Science and Mathematics 90(1): 1–12. Gottfredson, L. 1978. Race and sex differences in occupational aspirations: Their development and consequences for occupational segregation (Report No. 254). Baltimore, MD: Center for Social Organization of Schools, the Johns Hopkins University.

132 The Middle School Years ——— . 1981. Circumscription and compromise: A developmental theory of occupational aspirations. Journal of Counseling Psychology Monograph 28: 545–579. Green, R., J. W. Brown, and R. Long. 1978. Report and recommendations: Conference on mathematics in American Indian education. Washington, DC: Educational Foundation of America and American Association for the Advancement of Science. Gross, S. 1988. Participation and performance of women and minorities in mathematics. Vol. 1: Findings by gender and racial/ethnic group. Rockville, MD: Montgomery County Public Schools, Carver Educational Services Center. ———. 1989. Early mathematics performance and achievement: The beginning of a downward spiral for blacks and Hispanics. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA. Hall, P. 1981. Problems and solutions in education, employment and personal choice of minority women in science. Washington, DC: American Association for the Advancement of Science. Hall, V. C., A. Howe, S. Merkel, and N. Lederman. 1986. Behavior, motivation, and achievement in desegregated junior high school science classes. Journal of Educational Psychology 78(2): 108–115. Hart, L. E., and G. M. A. Stanic. 1989. Attitudes and achievement-related behaviors of middle school mathematics students: Views through four lenses. Paper presented at the annual meeting of the American Eduational Research Association, San Francisco, CA. Hill, O. W., W. C. Pettus, and B. A. Hedin. 1990. Three studies of factors affecting the attitudes of Blacks and females toward the pursuit of science and science-related careers. Journal of Research in Science Teaching 27(4): 289–314. Holland, J. 1973. Making vocational choices: A theory of careers. Englewood Cliffs, NJ: Prentice-Hall. Holmes, B. J. 1980. Black students’ performance in the national assessments of science and mathematics (Report No. SY–SM–50). Princeton, NJ: Educational Testing Service. Holtzman, E., R. Goldsmith, and C. Barrera. 1979. Field-dependence and field-independence: Educational implications for bilingual education. Austin, TX: Dissemination and Assessment Center for Bilingual Education. Jacobowitz, T. J. 1983. Relationship of sex, achievement, and science self-concept to the science career preferences of Black students. Journal of Research in Science Teaching 20(7): 621–628. James, R. K., and S. Smith. 1985. Alienation of students from science in grades 4–12. Science Education 69(1): 39–45. Jones, L. R., I. V. S. Mullis, S. A. Raizen, I. R. Weiss, and E. A. Weston. 1992. The 1992 science report card: NAEP’s assessment of fourth, eighth, and twelfth graders. Washington, DC: National Center for Education Statistics. Jones, L. V. 1987. The influence on mathematics test scores, by ethnicity and sex, of prior achievement and high school mathematics courses. Journal for Research in Mathematics Education 18(3): 180–186. Kagan, S., and G. L. Zahn. 1975. Field dependence and the school achievement gap between Anglo-American and Mexican-American children. Journal of Educational Psychology 67(5): 643–650. Kagan, S., G. L. Zahn, and J. Gealy. 1977. Competition and school achievement among Anglo-American and Mexican-American children. Journal of Educational Psychology 69(4): 432–441. Kahle, J. B. 1982. Can positive minority attitudes lead to achievement gains in science? Analysis of the 1977 National Assessment of Educational Progress, attitudes toward science. Science Education 66(4): 539–546.

Influences on Minority Participation in Mathematics, Science, and Engineering 133 ——— . 1983. The disadvantaged majority: Science education for women. Burlington, NC: North Carolina Biological Supply Company. Kahle, J. B., and M. K. Lakes. 1983. The myth of equality in science classrooms. Journal of Research in Science Teaching 20(2): 131–140. Kenschaft, P. C. 1981. Black women in mathematics. American Mathematical Monthly 88: 592–604. Kohr, R. L., J. R. Masters, J. R. Coldiron, R. S. Blust, and E. W. Skiffington. 1991. The relationship of race, class, and gender with mathematics achievement for fifth-, eighth-, and eleventh-grade students in Pennsylvania schools. Peabody Journal of Education 66(2): 147– 171. Krist, P. S. 1993. Educational and career choices in math and science for high ability African American women. Ph.D. diss., University of North Carolina at Chapel Hill. Lewellyn, R. J. 1990. Gender differences in achievement, self-efficacy, anxiety, and attributions in mathematics among primarily Black junior high school students. Ph.D. diss., University of Akron, 1989. Abstract in Dissertation Abstracts International, 50, sec. 7, 1989A. Lockheed, M. E., and K. S. Gorman. 1987. Sociocultural factors affecting science learning and attitude. In A. B. Champagne and L. E. Hornig, eds., This year in school science, 1987: Students and science learning, 41–66. Washington, DC: American Association for the Advancement of Science. Lockheed, M. E., M. Thorpe, J. Brooks-Gunn, P. Casserly, and A. McAloon. 1985. Sex and ethnic differences in middle school mathematics, science and computer science: What do we know? Princeton, NJ: Educational Testing Service. Lucy, W. 1976. An adult population reflects the stability of Holland’s personality types over time. Journal of College Student Personnel 17: 76–79. MacCorquodale, P. 1980. Psycho-social influences on the accomplishments of MexicanAmerican students. Paper presented at the meeting of the American Association of School Administrators, Chicago, IL. ERIC ED 200355. ———. 1984. Self-image, science and math: Does the image of the “scientist” keep girls and minorities from pursuing science and math? Paper presented at the 79th annual meeting of the American Sociological Association, San Antonio, TX. ——— . 1988. Mexican-American women and mathematics: Participation, aspirations, and achievement. In. R. R. Cocking and J. P. Mestre, eds., Linguistic and cultural influences on learning mathematics, 137–160. Hillsdale, NJ: Lawrence Erlbaum. Malcom, S. M. 1983. An assessment of programs that facilitate increased access and achievement of females and minorities in K–12 mathematics and science education. Washington, DC: American Association for the Advancement of Science, Office of Opportunities in Science. Malcolm, S. M., M. Aldrich, P. Q. Hall, P. Boulware, and V. Stern. 1984. Equity and excellence: Compatible goals. Washington, DC: American Association for the Advancement of Science, Office of Opportunities in Science. Marrett, C. B. 1986. Minority females in precollege mathematics: Towards a research agenda. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA. Matthews, W. L. 1981. Black females and mathematics: Barricade or bridge? Journal of Social and Behavioral Science 27: 88–92. ———. 1983. Influences on the learning and participation of minorities in mathematics (Final Report). Madison, WI: Wisconsin Center for Education Research. ——— . 1984. Influences on the learning and participation of minorities in mathematics. Journal for Research in Mathematics Education 15(2): 84–95.

134 The Middle School Years Matthews, W. L., T. P. Carpenter, M. M. Lindquist, and E. A. Silver. 1984. The third national assessment: Minorities and mathematics. Journal for Research in Mathematics Education 15(2): 165–171. Medin, J. A. 1985. Test anxiety, locus of control and mathematics achievement placement as correlates to achievement in mathematics by students in junior high school. Ph.D. diss., the American University. Mestre, J. P. 1981. Predicting academic achievement among bilingual Hispanic college technical students. Educational and Psychological Measurement 41(4): 1255–1264. ——— . 1986. The Latino science and engineering student: Recent research findings. In M. Olivas, ed., Latino college students, 157–192. New York: Teachers College Press. Milgram, J. 1992. A portrait of diversity: The middle level student. In J. L. Irvin, ed., Transforming middle level education, 16–27. Boston: Allyn & Bacon. Moore, C. G. 1982. The Navajo culture and the learning of mathematics. Washington, DC: National Institute of Education. ERIC ED 214708. Moore, E. G. J., and A. W. Smith. 1987. Sex and ethnic group differences in mathematics achievement: Results from the national longitudinal study. Journal for Research in Mathematics Education 18(1): 25–36. Morales, R., V. Shute, and J. Pellegrino. 1985. Developmental differences in understanding and solving simple mathematics word problems. Cognition and Instruction 2(1): 41–57. Mullis, I. V. S., J. A. Dossey, E. H. Owen, and G. W. Phillips. 1993. NAEP. 1992 mathematics report card for the nation and states (Report No. 23–STO2). Princeton, NJ: National Center for Education Statistics, Educational Testing Service. Mullis, I. V. S., and L. B. Jenkins. 1988. The science report card: Trends and achievement based on the 1986 national assessment (Report No. 17–S–01). Princeton, NJ: Educational Testing Service. Myers, D. E., and A. M. Milne. 1988. Effects of home language and primary language on mathematics achievement: A model and results for secondary analysis. In R. R. Cocking and J. P. Mestre, eds., Linguistic and cultural influences on learning mathematics, 259–293. Hillsdale, NJ: Lawrence Erlbaum. Nafziger, D., J. Holland, S. Helms, and J. McPartland. 1974. Applying an occupational classification to the work histories of young men and women. Journal of Vocational Behavior 5: 331–345. National Science Board. 1983. Educating Americans for the 21st century: A plan of action for improving mathematics, science and technology education for American elementary and secondary students so that their achievement is the best in the world by 1995. Washington, DC: National Science Foundation. Nelson, R. E. 1978. Sex differences in mathematics attitudes and related factors among AfroAmerican students. Knoxville, TN: University of Tennessee. Nielsen, F., and R. M. Fernandez. 1981. Achievement of Hispanic students in American high school: Background characteristics and achievement. Washington, DC: National Center for Education Statistics. Oakes, J. 1989. Tracking in secondary schools: A contextual perspective. In R. E. Slavin, ed., School and classroom organization, 173–196. Hillsdale, NJ: Lawrence Erlbaum. ———. 1991. Multiplying inequalities: The effects of race, social class, and tracking on opportunities to learn math and science. Santa Monica, CA: Rand Corporation. ———. 1992. Grouping students for instruction. In M. C. Alkin, ed., Encyclopedia of educational research, 6th ed., 562–568. New York: Macmillan. Okey, J. L., L. M. Snyder Jr., and G. Hackett. 1993. The broadening horizons project: Development of a vocational guidance program for eighth-grade students. The School Counselor 40: 218–222.

Influences on Minority Participation in Mathematics, Science, and Engineering 135 Olstad, R. G., J. R. Juarez, L. J. Davenport, and D. L. Haury. 1981. Inhibitors to achievement in science and mathematics by ethnic minorities. Seattle, WA: University of Washington. ERIC ED 233404. Orr, E. W. 1987. Twice as less. New York: W. W. Norton. Parsons, E. T., T. F. Adler, and C. M. Kaczala. 1982. Socialization of achievement attitudes and beliefs: Parental influences. Child Development 53: 310–321. Payne, B. D., J. E. Smith, and D. A. Payne. 1983. Sex and ethnic differences in relationships of test anxiety of performance in science examinations by fourth- and eighth-grade students: Implications for valid interpretations of achievement test scores. Educational and Psychological Measurement 43(1): 267–270. Persell, C. 1977. Education and inequality. New York: The Free Press. Powell, R., and J. Garcia. 1985. Portrayal of minorities and women in selected elementary science series. Journal of Research in Science Teaching 22: 519–533. Pulos, S., E. K. Stage, and R. Karplus. 1982. Setting effects in mathematical reasoning of early adolescents: Findings from three urban schools. Journal of Early Adolescence 2(1): 39–59. Rakow, S. J. 1985. Minority students in science. Urban Education 20(1): 103–113. Ramirez, M., and A. Castaneda. 1974. Cultural democracy: Bicognitive development and education. New York: Academic Press. Ramirez, M., and D. R. Price-Williams. 1974. Cognitive styles of children in three ethnic groups in the United States. Journal of Cross-Cultural Psychology 5(2):212–219. Reyes, L. H., and G. M. A. Stanic. 1988. Race, sex, socioeconomic status, and mathematics. Journal for Research in Mathematics Education 19(1): 26–43. Reynolds, A. J. 1991. Effects on an experiment-based physical science program on cognitive outcomes. Journal of Educational Research 84(5): 296–302. Rhone, L. M. 1989. Relations between parental expectation, mathematics ability, mathematics anxiety, achievement on mathematics word problems, and overall mathematics achievement in black adolescents. Ph.D. diss., New York University, 1989. Abstract in Dissertation Abstracts International, 50 sec., 12A: 3902–4036. Riggs, I. 1988. A review of factors related to gender and ethnic differences in math/science achievement levels of students K–12. Educational Considerations 15(1): 18–21. Roberge, J., and B. Flexer. 1983. Cognitive style, operatively, and mathematics achievement. Journal for Research in Mathematics Education 14(5): 344–353. Rosenbaum, J. 1976. Making inequality. New York: John Wiley. Rowe, M. B. 1977. Why blacks don’t pick science. Science Teacher 44(2): 34–35. Saxe, G. B. 1988. Linking language with mathematics achievement: Problems and prospects. In R. R. Cocking and J. P. Mestre, eds., Linguistic and cultural influences on learning mathematics, 47–62. Hillsdale, NJ: Lawrence Erlbaum. Scales, P. 1991. A portrait of young adolescents in the 1990s. Chapel Hill, NC: Center for Early Adolescence, University of North Carolina. Schreiber, D. A. 1984. Selecting appropriate attitudinal objectives for school science. Science Education 67: 595–603. Sherman, J. 1979. Predicting mathematics performance in high school girls and boys. Journal of Educational Psychology 71(2): 242–249. Sigel, I., and R. Coop. 1974. Cognitive style and classroom practice. In R. H. Coop and K. White, eds., Psychological concepts in the classroom, 250–275. New York: Harper & Row. Slavin, R. 1987. Ability grouping and student achievement in elementary schools: A bestevidence synthesis. Review of Educational Research 57: 293–336. ———. 1990. Achievement effects of ability grouping in secondary schools: A best-evidence synthesis. Review of Educational Research 60: 471–500.

136 The Middle School Years Smith, L. 1981. Mathematics education in an American Indian culture. Tempe, AZ: Arizona State University. Typescript. Smith, W. S., and T. O. Erb. 1986. Effect of women science career role models on early adolescents’ attitudes toward scientists and women in science. Journal of Research in Science Teaching 23: 667–676. So, A. Y., and K. S. Chan. 1982. What matters? A study of the relative impact of language background and socioeconomic status on reading achievement. Los Alamitos, CA: National Center for Bilingual Research. Stallings, J., and A. Robertson. 1979. Factors influencing women’s decision to enroll in advanced mathematics courses. Paper prepared for the National Institute of Education, Washington, DC. Steel, L. 1978. Origins of sex differences in high school mathematics achievement and participation. Palo Alto, CA: American Institutes for Research in the Behavioral Sciences. Strauss, J. B., and L. Rainwater. 1962. The professional scientist: A study of American chemists. Chicago: Aldine. Talton, E. L., and R. D. Simpson. 1985. Relationships between peer and individual attitudes toward science among adolescent students. Science Education 69(1): 19–24. Thomas, G. E. 1982. Choosing a college major in the hard and technical sciences and the professions: A causal explanation. Baltimore: Johns Hopkins University, Center for Social Organization of Schools. ——— . 1984. Determinants and motivations underlying the college major choice of race and sex groups. Washington, DC: National Science Foundation. ———. 1986. Cultivating the interest of women and minorities in high school mathematics and science. Science Education 70(1): 31–43. Treisman, P. U. 1992. Studying students studying calculus: A look at the lives of minority mathematics students in college. College Mathematics Journal 23: 362–372. Tsai, S., and H. J. Walberg. 1983. Mathematics achievement and attitude productivity in junior high school. Journal of Educational Research 76(5): 267–272. U.S. Congress, Office of Technology Assessment. 1988. Elementary and secondary education for science and engineering: A technical memorandum (OTA–TM–SET–41). Washington, DC: U. S. Government Printing Office. Vaidya, S., and N. Chansky. 1980. Cognitive development and cognitive style as factors in mathematics achievment. Journal of Educational Psychology 72(3): 326–330. Valverde, L. A. 1984. Underachievement and underrepresentation of Hispanics in mathematics and mathematics-related careers. Journal for Research in Mathematics Education 15(2): 123–133. Walker, C. L., and S. J. Rakow. 1985. The status of Hispanic American students in science: Attitudes. Hispanic Journal of Behavioral Sciences 7(3): 225–245. Willig, A. C., D. K. Harnisch, K. T. Hill, and M. L. Maehr. 1978. Sociocultural and educational correlates of success-failure attributions and evaluation anxiety in the school setting for Black, Hispanic and Anglo children. American Educational Research Journal 20(3): 385–410. Witkin, H., and D. Goodenough. 1988. Cognitive styles: Essence and origins (Field dependence and field independence). Madison, CT: International Universities Press. Witkin, H., C. Moore, D. Goodenough, and P. Cox. 1977. Field-dependent and field-independent cognitive styles and their educational implications. Review of Educational Research 47(1): 1–64. Witthuhn, J. 1984. Patterns of student performance on mathematics strands for American Indians and others. Journal of Experimental Education 53(1): 58–63.

Influences on Minority ParticipationAdvancing in Mathematics, MiddleScience, Grade Reform: and Engineering Research 137 Yong, F. I. 1992. Mathematics and science attitudes of African American middle grade students identified as gifted: Gender and grade differences. Roeper Review 14(3): 136–140. Young, H. A. 1981. Retaining Blacks in science: An affective model. In G. E. Thomas, ed., Black students in higher education: Conditions and experiences in the 1970s, 261–271. Westport, CT: Greenwood Press.

anita m. baker

Advancing Middle Grade Reform: Research Positive Factors Factors that have a positive impact on the participation and achievement of minority and female students in mathematics and science. The work of the Urban Schools Science and Mathematics Program (USSAMP) helped to identify factors influencing the teaching and learning of mathematics and science and to develop strategies to change and improve these factors. USSAMP was a program jointly sponsored by the Ford Motor Company and the U.S. Equal Employment Opportunity Commission and coordinated by the Academy for Educational Development. The program was initiated in response to growing concern about the underrepresentation of African Americans, Latinos, and females in precollege mathematics and science courses and in careers in science and technology, particularly in the automotive industry and specifically at Ford. The Ford Motor Company awarded grants to three urban school districts to support activities to improve the mathematics and science achievement of African American, Latino, and female middle grades students in three cities where Ford had a large corporate presence: Atlanta, Cleveland, and Detroit. For two years participating schools in the districts undertook a number of innovative activities to enhance the teaching and learning of mathematics and science. USSAMP was guided by a vision of equity, namely, that African American, Latino, and female students could reach high levels of achievement in both mathematics and science given the right combination of high teacher expectations and effective curriculum and teaching strategies. It was also grounded on two important assumptions: (1) improvement strategies should focus on the middle grades, where science and mathematics curricula become more challenging and where mathematics choices and performance can

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become decisive factors in future educational and career choices; and (2) algebra is a critical factor in determining students’ access to advanced high school mathematics and science courses and to substantive careers in science and technology. The program was designed to address several factors considered to be critical barriers to the participation and achievement of minority and female students in mathematics and science courses and careers. These included: • outmoded middle grades mathematics and science curricula • insufficient preparation of middle grades mathematics and science teachers, and large numbers of teachers teaching out of field • lack of ongoing professional development in mathematics and science • absence of physical science and algebra in the middle grades curricula • lack of resources • insufficient guidance and support services for students • insufficient career counseling • tracking of students away from algebra and other advanced mathematics and science courses

Components of the Strategy Three major components formed the basis of the USSAMP strategy: 1. revised mathematics and science curricula, focusing on problem solving, higher order thinking skills, integrating new technology into the classroom, and making instruction and content relevant to daily life and to the workplace 2. effective professional development, including training on revised curricula aimed at increasing knowledge of new approaches in teaching mathematics and science and changing teachers’ perceptions of the abilities of African American, Latino, and female students in these subjects 3. improved academic and nonacademic supports, including counseling, career-awareness activities, mentoring, tutoring, and motivational techniques Each district undertook a number of specific approaches and strategies to meet program goals. For example, in Atlanta the program was operated in two schools that, in conjunction with local colleges, conducted: a Saturday Science Academy for teachers and students, both separately and together; an eight-week intensive summer program for high school students; a summer enrichment program for students; and a series of workshops as well as summer programs for teachers sponsored by a local university and Southeastern Consortium for Minorities in Engineering (SECME). An algebra curriculum and guidebook were also developed. The Cleveland project was piloted in two schools where: an activity-based, student-centered program of instruction was developed; science and mathematics clubs, fairs, and competitions were held; career-awareness and self-esteem activities were conducted; and workshops were held for parents. In Detroit five schools participated in the program, which:

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increased the amount of time devoted to mathematics and science in the target schools; made mathematics and science curricula more activity-based and experiential; increased student awareness of the contributions made by Africans, African Americans, Latinos, and women to mathematics, science, and technology; provided a transition to algebra; focused on cooperative learning; and provided students with career-awareness supports through guidance and field trips. At the end of the initial project, preliminary evaluations revealed positive outcomes. By addressing the preceding factors, the projects had helped most teachers at the pilot schools integrate activity-based instruction and self-esteem and career awareness into their educational programs. The sites also concluded that the projects had generated enthusiasm for mathematics and science among teachers and students alike, and improved student knowledge of possible careers in science and technology. Teachers and administrators involved with the project pointed out that students have to be helped to construct mathematics and science knowledge from their own experiences and to see that what they learn has meaning in their everyday lives. This required continuous, intensive, and enhanced professional development to provide teachers with ongoing opportunities to keep abreast of the latest applied research and to increase their knowledge of appropriate curricula and instructional strategies. It also required a substantial investment of time for planning and collaborations among teachers. The resulting enhanced curriculum, pedagogy, and delivery system was then targeted to increase opportunities for teachers to learn in areas where research shows that differences still exist, namely, in preparing all students for upper-level mathematics, physics, and chemistry courses.

Stages of Reform Efforts to improve teaching and learning in mathematics and science should be integrated with the broader, second-wave middle grades reform. Research must also focus on how to expand the results of promising interventions into all classrooms and integrate efforts to reform mathematics and science education with ongoing middle grades reform efforts. Middle grades education has blossomed over the last ten years. Many major foundations—the Carnegie Corporation, the Lilly Endowment, and the W. K. Kellogg and Edna McConnell Clark foundations—have initiated such reform. Carnegie, in its Middle Grades School State Policy Initiative, has paid particular attention to state policy in the systemic-reform picture but has not emphasized the role of the local districts. The Clark Foundation has focused on the local district’s role in orchestrating and supporting reform. Lilly has tried to transfer reform from the middle grades to other levels of the educational system. All these foundations are trying to advance middle grades reform to a “second wave.” The first wave essentially consisted of efforts to reorganize junior high schools structurally into more appropriate learning environments for young adolescent students. This included breaking schools up into “houses,” where students could develop closer attachments to fewer adults, and creating “advisory groups.” Although these are important changes, the second wave of reform must go beyond reorganization to focus on student learning and achievement, particularly of poor and low-

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achieving students. The second wave must also emphasize systemic educational reform not limited to a single classroom, school, or pedagogical approach but promoted and supported by a comprehensive set of policies and practices at the state, district, and school levels. The middle grades reform movement, particularly that which has been promoted by national associations (such as the National Middle School Association and the National Association of Secondary School Principals), has tended not to consider the special needs and circumstances of schools serving children of color, poor students, urban students, immigrant students, language-minority students, and other groups. The foundations are paying particular attention to such students and the schools they attend; as a result, they are creating a new and emerging body of knowledge of effective middle grades education for all students. As the second wave develops and is directed increasingly at teaching and learning and the needs of the special populations previously identified, overall achievement and within desired skills and knowledge areas will become the focus of new initiatives and research. Educators interested in improving the teaching of mathematics and science must continue to monitor the effectiveness of their programs and increasingly tailor their efforts to link them to the broader, second-wave reform efforts.

deanna beane

It Takes a Village to Raise a Scientist In “Influences on Minority Participation in Mathematics, Science and Engineering,” Clewell and Braddock quite appropriately introduce their review of the research with a brief overview of the psychological development of adolescents. For the practitioner, as well as the reseacher, the academic achievement of adolescents should not be explored in total isolation from the findings of behavioral scientists. There is a considerable body of knowledge on the physical, cognitive, and psychosocial development of children as they make the transition to young adulthood. To facilitate the cognitive development so important to success in mathematics and science, it is imperative that we become more consistent in designing educational environments and experiences based on what we are learning from cognitive science and what we now know about the physical, social, and emotional developmental needs of young adolescents.

Influences on Minority Participation in Mathematics, It Takes a Village Science, to and RaiseEngineering a Scientist 141

However, when looking at the range of risk factors confronting American Indian, African American, and Latino adolescents, the practitioner lacks access to reliable empirical research that identifies developmental needs unique to these adolescents: They are struggling to answer the universal key questions of youth: “Am I competent? Am I normal? Am I lovable and loving?” But their struggle must play itself out on the stage of a macroculture that has historically devalued these populations and denied them access to quality educational opportunities. Whether or not their families have the resources to supplement the formal education with structured out-ofschool activities, all adolescents will search for the answers to those universal questions. Without structured activities, they can end up seeking the answers in the wrong places or through self-destructive behaviors. Competency in mathematics, science, or other disciplines can help them in their quest to figure out where they fit in the world and what they can contribute. Myriad out-of-school intervention programs have evolved to level the playing field so that more of these adolescents can join the math/science talent pool. Many of these programs have been designed not only to encourage academic achievement but also to address the general developmental needs of adolescents and the overlay of other more subtle needs imposed because of the minority status of the students. Frequently one finds that not only highly motivated students but those who are less motivated emerge from these programs with a newfound sense of efficacy and competence. Some students go on to concentrate in the sciences, and others who previously were underachievers perform better academically; most become more aware of the relationship between advanced math and science courses and future education and career options. For young adolescents from low-income families—too many of whom are already on a declining academic path—what kinds of formal and informal intervention during the middle school years can effectively capitalize on their positive attitudes toward mathematics and science? Can we use their interest in the natural world as a springboard for the development of skills that equip them for higher achievement? Is this an opportunity to determine whether there are gender-based differences between the developmental needs of young minority adolescents? If such differences are identified, do they merit the attention of school and community practitioners concerned with the academic performance and dropout rate of Latino and African American males? Can we apply the research on attitudinal and achievement factors to develop mathematics and science programs and curricula that more effectively meet the developmental needs of minority students? At present, practitioners rely largely on anecdotal evidence for affirmative answers to these questions. The potential impact of science programs that focus on the adolescent learner is demonstrated by YouthALIVE! (YOUTH Achievement through Learning, Involvement, Volunteering and Employment) programs at fifty-two museums throughout the United States. Over 7,000 young people, ages ten to seventeen, have spent their free time in structured enrichment or work-based learning programs developed by local science or children’s museums. While it is customary to find teenagers from middle-class families enjoying the enrichment classes and volunteering in museums, it has been quite rare to see young people from minority and low-income families engaged in museum activities—except on the annual class field trip. Two examples

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of YouthALIVE! programs are particularly appropriate for this discussion because both have involved the same group of adolescents for several years. In South Central Los Angeles, the California Museum of Science and Industry (now the California Science Center) began its YouthALIVE! program, the Curator Kids Club, in 1993 with twenty-five fifth-graders from Avalon Gardens Public Housing Community. The young African American woman who was given the task of designing this program had no experience in proposal development and very little in science education but she had loved museums as a child and was committed to sharing that love with a new generation of youngsters. Teams of parents volunteered to assist with science activities on Saturday mornings. Several times a year the Curator Kids, all of whom were African American and Latino, hosted a community science day at the museum to explore with friends and family members some of the science concepts they had learned through the hands-on activities. The parents at first viewed the museum and the science program as out-of-school support to protect their children from the streets. Soon they noticed that since joining the Curator Kids Club, their children were becoming “smarter in school.” A year later, school teachers were asking the museum to take on more youngsters. Avalon Gardens’ original group of Curator Kids are now looking forward to joining a workbased learning program in the museum that will allow them to explain exhibits to museum visitors, engage them in science activities, conduct science demonstrations, and eventually earn money. The second program, located at the Louisiana Arts and Sciences Center in Baton Rouge, also began in 1993 but its participants were thirty fifth-grade African American girls, most of whom were from low-income families. They were paired with high school girls who were successful in their science courses. Two weeks of science activities and field trips in Summer Science Camp were followed by monthly activities at the museum during the school year. During a field trip to visit an African American chemist in her lab, one fifth-grader pointed out that among the many photos in the lobby there were none of women or African American people. Everyone, including the project director, was shocked, but the chemist’s positive response communicated to the girls her own goals and sense of efficacy: “One day you will come back and see my picture there.” We knew how powerful that experience must have been when, on a later occasion, one of the girls demonstrated her own budding efficacy. As she and her friends were setting out for another field trip in the museum’s van, a boy was heard to comment that the van was full of “retarded girls.” Although she had never spoken up for herself before, the girl answered, “No, we’re future scientists!” She and the others are now preparing to assist the younger cohort who will be joining the program next summer. In this time of reform in mathematics and science education, what lessons can schools and their communities extract from the experiences of intervention programs that are effectively engaging young adolescents? Since young adolescents spend only 31 percent of their waking hours in the classroom, serious attention must be given to the 42 percent that is considered discretionary time spent out of school. For most adults who have pursued careers in science, their early interest was nurtured during the discretionary hours by a relative, special teacher, or family friend and by informal science experiences—Boy Scout activities, museum and zoo visits, science fair

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projects, family trips, and hobbies. However, this is not usually the case for minority and low-income children. Clearly, intervention programs can provide that type of support for some adolescents, but not for most. For most minority and low-income adolescents, it is largely the school experience that must level the playing field. Can middle schools and community-based organizations come together to set academic achievement for every student as a goal? How can they collaborate to assure that the following essential developmental needs of young adolescents are met? • Meaningful participation or engagement in work useful to the school or community • A sense of competence or achievement, particularly in mathematics and science • Multiple opportunities for self-exploration and development of a sense of identity • Positive social interaction with peers • Stable relationships with caring adults • A variety of learning opportunities, formats, instructional materials, and groupings • Structure and clear, reasonable rules • Sufficient physical activity In the middle years of childhood, perhaps more so than any other period, it really does take a village to raise a child, especially if that child is a future scientist.

nancy carson

The Policy Perspective

Over the past decade, given the increasing attention paid to participation in science and math, a good deal has been learned about the exposure, achievement, experience, and attitudes of minority children—as well as all children—from the fourth through the eighth grades. This is where the pipeline (to use a somewhat unfashionable but still sound metaphor) tends to narrow precipitously. By the end of this period (and sometimes even at the beginning), many children have slipped below grade level in achievement. Research clearly shows that once a child falls behind, he or she is unlikely to recover. Many children who have thus far not failed in achievement now acquire

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negative attitudes about the utility or appropriateness of the math/science/engineering path. High-ability minority children must prove themselves yearly to new, skeptical teachers. Peer pressure and gender issues strike with a vengeance, and schools are faced with complex structural problems. Children in the sixth or seventh grades are making choices that directly limit their futures—often without realizing it. In short, from the policy perspective this is where it breaks your heart. In discussing policy, it is always useful to be clear about exactly what problem we are trying to solve, lest we become enchanted with elegant solutions that do not meet current needs. The problem here is that too many children are at the proverbial point of no return for futures in science and math by the end of their middle years. School policy must reverse this trend. A corollary issue, which will be implemented further down the pipeline, is the need for strategies to encourage reentry to a future with science and math for those who want a second chance. The number of students wishing to reenter is likely to be small, so it is of much greater importance to eliminate the situations that lead to drastically reduced student options. Today the system allows far too many students to make life-altering choices at a very young age, during a period of great personal and institutional turmoil, without an adequate understanding of what is being lost—and without any experience of success. Now that the importance of the middle years has been established, there have been a number of initiatives designed to improve the success rate. Small, pilot intervention programs that have become the basis for many broader programs mirror the childhood-development patterns of this age group in their diversity, flexibility, and responsiveness to the immediate needs of the children. It seems clear that the experience of successful intervention programs is the basis for approaches such as the National Science Foundation’s Systemic Initiative; a large-scale commitment and a desire to achieve fundamental change is required, but no particular process or approach is essential for success. The states and cities are up against the reality of learning, and the theory is that the Systemic Initiatives will both require and support change through whatever means the states and cities conclude is best for their students. Much work has also gone into trying to prevent early academic tracking. One approach leads to programs that intentionally mix ability groups in classes. This work has been intellectually satisfying but has not been integrated well into the daily routine of schools. While researchers understand and are comfortable with mixed-ability arrangements, parents and public school administrators face many pressures and beliefs that work against putting this policy into action. Parents of high-ability students (as measured by standardized tests and traditional grades) almost always resist this option, as do many school administrators who are trying to retain these children in the public schools. The intellectual arguments against tracking do not translate into good sound bites; they make parents uncomfortable in that they are nonintuitive and nontraditional. My own belief is that we must continue to find ways to overcome traditional ability-group tracking because this effort will benefit all children; as this struggle proceeds, we must identify strategies that keep options open for children at several ability levels. In other words, the perfect can become the enemy of the good here. There is a big difference between being not quite so proficient in a subject—say, algebra—and being on a path that precludes any algebra at all. Working with sci-

Influences on Minority Participation in Mathematics, Science, The Policy and Engineering Perspective 145

ence and math teachers and relying on new standards, we must find more paths to the same destination even if some may follow a longer route. We must focus on the objective, which is that decisions made at the middle school level must not preclude desirable life choices for learning or work, including science and math options. We are now beginning to see the results of assigning more math and science courses to all children as a requirement for graduation. Despite many difficulties and much apprehension, competency levels are rising, and this is translating into better scores and increased ability among those applying for college admission. Students are just one part of the policy equation, teachers being the other component. When the achievement of minority children is evaluated, focus often centers first on what is currently called teaching for diversity. The greater heterogeneity of today’s students requires teaching that is very different from that of the past. The thrust of the traditional approach was extrapolated from techniques that worked for generations of white male students with educated parents, but this is certainly not an adequate model for today’s teachers. Teachers must sincerely believe that all their students can learn, and they must possess the experience and resources to find ways to reach each child. One wonders whether problems surrounding teaching practices may be generated as much by the science backgrounds of teachers as by inappropriate pedagogy. It is difficult to inculcate a love of science and a sense of skill if you do not possess these yourself. It is common knowledge that almost all elementary school teachers are either badly trained in science or, more likely, not trained at all, and many elementary school teachers have no knowledge of mathematics. I suspect that middle school teachers do not have strong backgrounds in these subjects either. Let me stress here the importance not only of the science teacher but teachers who can understand and support the scientific method, demonstrate for students the ability to take on and master quantitative disciplines and complex problem-solving methods, and underscore the value of science in understanding the world. One of the great experiments underway today is the Chicago Academy effort, where a team led by scientists is trying to retrain virtually all of the science teachers in the Chicago system. This approach is based on the hypothesis that only a larger-scale effort can transform the schools; science teachers must become an integral part of the scientific community for this effort to flourish. It is useful to remember that even for the majority culture, Americans in general do not care for or understand science, do not acquire science skills early or adequately, and hold many negative views about who does science and why. To be effective, any new policy must address these two issues: (1) new teachers need to understand science as well as diversity; and (2) teachers already in schools must be retrained and supported in a coherent and continuing manner. To turn to another initiative focused on middle school children, consider the effect on attitudes and skills of children resulting from math and science clubs and similar activities, particularly those calling on supportive, nonschool adults. A good example would be a science club or science exposition project that meets after school and involves members of a local engineering society or members of a women’s science group. These situations provide students with several components that cognitivedevelopment research has certified as being effective: the use of positive role models; attention to skill development at whatever pace the student can sustain; a focus on

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task comprehension and completion; reinforcement for a job well done; doing science for the pleasure of understanding and knowledge attainment; an opportunity to apply abstract knowledge in practical situations; and positive feedback. In addition, many of these special situations build upon the examples of the past decade, striving for achievement and distinction, not remediation. Some students do much better in these environments than they do in the classroom; their achievements here can alter the unconscious attitudes of a teacher, who may subsequently become more supportive. Most important of all, the children discover the excitement of science and develop a belief in their own ability to learn and do. Enrichment programs are occasionally at odds with certain philosophies of school management because they draw on resources outside traditional boundaries and are not available to all schools. I believe it is best to reward success wherever we find it, letting it spread to other environments, and not worry overly about what is not being done. More support and reinforcement for these efforts is needed. My own view about the need to support these programs parallels another belief that is not widely shared in the research community. A great deal of energy has been spent on finding ways to transfer intervention programs directly into other classroom settings. My personal conclusion is that many intervention programs can never be transferred successfully; it works precisely because it is an intervention, or because the leadership is particularly strong or well suited to the classroom. Rather than lament this fact, I believe we should applaud it. The children who benefit from the intervention are much better off, and frequently the school itself is improved. In addition, too much policy time is spent on trying to figure out in exquisite detail just what is “exportable” or will transfer easily. The real problem is that we know we could develop effective programs in other classrooms, but we do not provide adequate resources and mount the necessary effort to do so. The solution to the problem is not a mystery; it simply requires commitment. Intervention programs, both in their specialization and their universality, offer an excellent model for another approach that is taking root, namely, communitybased programs. We cannot expect schools single-handedly to accomplish all that needs to be done, and sometimes a library or church-based program can muster local resources and fill in educational gaps. This enables the resources of the community to be mobilized and allows for contributions that could not (or would not) be made in the more restrictive school environment. Such programs may also be more realistic in terms of involving working parents because of their hours of operation or convenient location. As fewer dollars flow to schools, community groups can help fill the gaps; these efforts should be understood as contributing to a whole community of learning, only a portion of which is formal schooling. Finally, current policy discussions must focus attention on the power and impact of technology on middle school students. For many years the best educational software has been in the math and science area, where simulation techniques open up avenues of understanding that cannot be duplicated in any classroom. Technology offers nonjudgmental learning, pacing based on individual needs, a flexible learning environment either in or out of school, and connections to vast resources. Technology also serves and supports teachers, providing resources they cannot easily obtain otherwise. We have only recently begun to understand how crucial it is to give teachers

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the time, tools, and techniques they need to effectively integrate technology into teaching—all of which costs money. But properly applied technology expands faculty resources by adding to the teaching process in many ways. Research on the effectiveness of technology shows that it offers particular advantages to students at risk of school failure. Technology crosses boundaries, allows students to become independent learners, and affects teaching. In terms of capability, technology is cheap and getting cheaper all the time. The coupling of technology with community-based programs is growing across the country, bringing together vital tools that support and extend the learning community. Early in this essay I said that seeing children fail in the middle years breaks your heart. It breaks your heart because children at this stage still show enthusiasm for learning, desire to work hard and succeed, and hopefulness about the future. Indeed, they demonstrate in many ways that the problem lies not with the children but with the learning environment and the living situation, which lead to failure. We cannot improve every child’s living situation and perhaps not every child’s learning environment, but we can and must try to reverse these trends through innovative and effective policies.

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iv

ADOLESCENCE ONWARD

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angela b. ginorio and jeri grignon

The Transition to and from High School of Ethnic Minority Students

n this chapter we will explore how the high school experiences of ethnic minority

I students translate the statement “when I grow up I want to be a scientist/mathema-

tician/engineer” into skills that make a college admission and a major in a science, mathematics, and engineering (SME) field a possibility. Students enter high school in middle adolescence at either the ninth or tenth grade and leave high school as young adults three or four years later. By staying in high school, students are fulfilling the principal social duty of adolescents as it has been defined in Western culture: the acquisition of an education (Feldman and Elliot 1990). We begin our purview with ethnic minority students at the eighth-grade level, poised to enter high school, and we will follow them through their decision about their first year after high school. They leave behind them years of growing up that have included personal, cognitive, and social developmental tasks in school, home, and community. But the task that defines adolescence is one of gaining increasing independence and establishing one’s own identity in the world. The adolescent must select from all the possible selves and integrate them into her or his own identity while maintaining continuity with the past (Harter 1990). Thus, one of the major tasks confronting students during these years is that of occupational choice. The occupational choice of interest in this chapter is that which promotes or discourages education and eventual work in SME.

Our gratitude to Michelle Elekonich and Devamonie Naidoo, without whose invaluable work this chapter could not have been completed. Many thanks to Beatriz Chu Clewell for sharing resources, to Michelle Huston for help with summaries, and to Susan Glenn, Caroline Chung Simpson, Priscilla Wald, and Shirley Yee for their comments. Thanks also to Linda Carlin, Kathryn Donald, and Barbara Schneider for their contributions.

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In American society age, gender, and occupation (student, nonstudent), are the traditional markers of identity for people between the ages of thirteen and nineteen. Ethnic minority teenagers have always had to include a racial/ethnic component when establishing their self-identity. Society at large as well as social scientists are beginning to address ethnoracial identity as a central issue in development for all teenagers in this country, one that had gone unacknowledged for majority youth. Ethnoracial identity denotes not only physiognomy and ethnicity but also culture (including language and religion) and immigrant status. While these factors are salient enough for many people to link them to their sense of identity, these are not the only demographic markers that shape identity in this culture. Most frequently ignored in the United States by the lay public and social scientists alike is socioeconomic class. In addition, while sexuality is coming to the fore during adolescence, both its behavioral and orientational aspects are often taboo. In this chapter we will examine the factors that enter into ethnic minority teenage students’ decisions about career options, including SME fields “within the context of a complex social reality that presents each individual with a wide variety of choices, each of which has both long-range and immediate consequences” (Eccles 1994:591). We may add that these choices have both immediate and long-term antecedents of both a personal and social nature that make the choices varyingly accessible to individual students. Not all students perceive the “wide variety of choices” that Eccles refers to, and the students that perceive these choices may realize that they are not positioned to have access to them. Thus, the task is not only about choosing; choice must be preceded by learning about choices, and followed by realistic opportunities of gaining access to them. Our focus will be on attitudes, interests, achievement, and performance of students as affected by family, school, and community at each of three stages of educational development: eighth grade, high school, and the transition to the first year after high school. This framework facilitates an examination of individuals’ achievements and failures nested in increasingly larger circles of family, school, and community. We will attempt to make evident the multiple strands that tie seemingly individual experiences and choices to cultural and structural variables. The existing literature on ethnic minority students and SME has improved in recent years in terms of disaggregating data and reporting on issues both by ethnicity and gender, making analyses and conclusions more accurate. This literature usually addresses issues related to underrepresented ethnic minority students in SME thus excluding in most cases Asian Americans. When available, information about Asian Americans has been included here.

Middle School / Junior High School While an occupational decision is still years ahead, eighth graders already have some basis—not all of it of their own making—for occupational choice. Thirteen or more years of socialization by family, school, and society have developed skills and attitudes that have begun to narrow teenagers’ occupational choices—away from SME fields.

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Students Attitudes and interests. By the eighth grade, interest in SME has peaked or is close to peaking for students in the United States (Berryman 1983; James and Smith 1985). Clewell and Braddock provide a complete summary (in this volume), but here we want to note that African American and Latino/Latina students are less confident than white students about their ability to do science and mathematics even though they may have more positive attitudes toward mathematics and science than their white classmates. Using National Assessment of Educational Progress (NAEP) data, Jones and colleagues (1992) reported that Hispanic and African American students valued the usefulness of science classes more than their white peers. Usefulness is defined as a measure of a student’s beliefs about the current usefulness for everyday work of mathematics or science and in relation to his or her future goals (Fennema and Sherman 1976). While black students’ attitudes toward science and mathematics are as positive as those of white students, their achievement is lower—although the gap has been closing (Mullis and Jenkins 1988; Dossey et al. 1988). Gender also affects these outcomes, with girls having less positive attitudes toward mathematics than boys across all ethnic groups. Thus, the relationship between self-concept and performance as predicted from white samples does not hold for ethnic minority students, leading us to conclude that racial stereotypes affect students’ self-concept as potential “doers” for mathematics and science. Achievement and performance. In junior high school, the entering scores in reading and mathematics for both white and black students are the best predictors of their grades in science courses (Howe 1982). In the eighth grade, 45 percent of whites and 38 percent of Hispanics are enrolled in algebra or accelerated mathematics (Ramírez 1993), but very few students in a low socioeconomic status (SES) score in the advanced mathematics category while very few high SES students fall below the basic level mathematics. Low SES girls do better than low SES boys, but high SES girls perform at the same level or lower than high SES boys (AAUW 1992). While black girls’ grades are very similar to those of black boys, American Indian, Asian American, and Hispanic girls get better grades than Hispanic boys. When analyzing the data within the same socioeconomic status, ethnic differences persist, with Asian Americans and whites doing better than Hispanics and African Americans, particularly at the high SES level (Burbridge, as cited in AAUW 1992). SES affects other aspects of a student’s experiences of success. By eighth grade a third of all boys have been held back at least one grade. But this figure hides the fact that the majority of those boys are of low SES. For example, the number of low SES boys held back ranges from 26 percent for Asian Americans to 41 percent for American Indians, while the number of high SES boys held back ranges from 5 percent for Asian Americans to 17 percent for American Indians. There is a greater similarity among the various ethnic groups at the same SES level than there is between high and low SES students of the same ethnicity. In all groups except African Americans, percentages for girls of any SES are much lower than for boys (National Education Longitudinal Study [NELS] data, as cited in AAUW 1992). Thus, the relationship between SES and outcomes is mediated by both ethnicity and gender.

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Schools There is great variation in the levels of tax support that schools receive from state to state and from community to community. Thus, achievement scores reflect not only the effects of the individual student’s SES, but also the effects that result from the level of support received by the school. Black and Hispanic students are more likely to attend schools in central cities or rural areas—schools that tend to have lower levels of support, less access to resources (Fairchild 1984), and sometimes less qualified teachers. In addition, students in low SES schools use the resources in ways that are less effective; for example, computers are used for drills rather than for experiments (Miura, as cited in Oakes 1990). To the degree that students have less educational resources at home (Ekstrom, Goertz, and Rock 1988), the experience they have in schools is more significant. Evidence for this can be seen in Grignon’s (1993) finding regarding eighth-grade Menominee girls’ experiences with computers, which in school were equal to those of Menominee boys but in the home environment were less than those of boys. By the twelfth grade, females were enrolling in fewer computerrelated classes than were boys. When experiences outside the school setting are not the same, having passive equal access in school may not be enough to correct the imbalance. A lack of experiences outside the school setting may limit the kinds of questions students ask or the level of interest they bring to the classroom. Teachers need to be not only responsive but also proactive in their approach to students. To summarize, ethnic minority middle school students are clearly interested in and value science and mathematics. Girls tend to value mathematics less than boys in all ethnic groups. However, positive attitudes toward science and mathematics are more closely related to achievement among girls than among boys. In general, at this point in their education girls of all ethnic groups achieve at higher levels than boys of the same ethnicity. It might be more meaningful to talk about SES rather than ethnicities, as there is a greater similarity among ethnic groups at the same SES level than within one ethnicity across all SES levels. The relationship between SES and academic achievement and advancement is mediated by both ethnicity and gender of individuals and structurally by the tax-based resources of a school.

High School Most American teenagers spend the middle years of adolescence in high school (NCES 1995b). High school marks the beginning of a time when visions of life beyond school loom just over the horizon. Possible selves shaped by different occupations are imagined and used in the process of making choices in high school. These academic decisions may have long-term implications. At the high school level, the challenge related to SME careers is fourfold: • to retain students over the four-year period • to translate the student’s initial interest into actually taking science and mathematics classes

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• to sustain their curiosity such that they would choose careers in science and technology • to offer sufficient advanced-level courses to adequately prepare them for college entry and facilitate their retention in these majors once they get into college. Students Attitudes and interests. If attitude or interest was all that it took to succeed in mathematics or science, ethnic minorities would not lag as far behind in terms of participation as they do at present. Grandy (1987) reports that among blacks who take the Scholastic Achievement Test (SAT) interest in science is almost as high as that of white SAT takers. There are gender differences in stated interest in the various fields of SME among underrepresented ethnic minority groups. In all groups more females than males planned to major in biological sciences, while the opposite was true of engineering and physics for all groups. Interest in mathematics varied according to ethnic group; more African American and Puerto Rican females than males were interested in mathematics, while among American Indians, Latin Americans, and Mexican Americans more males than females were interested (Clewell and Ginorio 1996). An earlier report indicated that blacks have the most positive attitudes toward science of any group in the United States (Hueftle, Rakow, and Welch 1983). On the other hand, Anderson (1989) reported that relative to white students, black students were not as supportive of scientific research nor as convinced of its utility in solving the world’s problems. Regardless of ethnicity, both African American and white students with science-related hobbies are more likely to have a high interest in high school science (Thomas 1986), highlighting the importance of extracurricular activities. Campbell and Perry (1988) reported that ethnic minority students had less positive attitudes toward learning computer skills than did whites. As this group of studies makes evident, results about attitudes toward science among African Americans are not uniformly consistent. These differences might be accounted for in terms of methodological issues related to assessment, or they may reflect actual differences resulting from the samples used (African Americans are not a homogeneous population even though much research treats them as such) or even the year or region in which the studies were done. Student enrollment in high school mathematics and computer coursework is related to self-confidence (Armstrong and Price 1982; Bear, Richards, and Lancaster 1987; Eccles, MacIver, and Lange 1986; Handel 1986; Lantz and Smith 1981; Sherman 1979; Visser 1986), but it is even more strongly related to perceptions of usefulness (Armstrong and Price 1982; Eccles, MacIver, and Lange 1986; Handel 1986; Meyer and Kohler 1990), especially for advanced mathematics. However, the relationship of self-confidence to competency is not the same for all ethnic groups. For white students confidence (“I’m good at mathematics”) is positively related to competency, while for black students high confidence is not equated with high competency (Dossey et al. 1988; Gross 1988). Similarly, in computer education black

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students had significantly higher levels of self-confidence than their nonblack classmates, but this did not translate into higher performance. The lack of correspondence between levels of self-confidence and performance for ethnic minority students echoes that reported for middle school, reinforcing the need to examine more carefully the assumptions built into models of achievement and their relationship to self-confidence. There is evidence that some low SES African American and Latino males may not value the education they are receiving. As will be discussed in the section on schools below, black males get a high degree of criticism in high school (Hrabowski and Pearson 1993). As Patthey-Chavez (1993) writes, referring to Latinos/Latinas: “Latino adolescents are highly motivated, but their expectations of success are colored by experiences of hostility and discrimination from the society at large. . . . [T]hey do find it difficult to cooperate in the educational enterprise. Many of them simply leave it altogether” (56). Quoting a 1986 report by Ogbu and Matute-Bianchi to the California State Department of Education, Patthey-Chavez goes on to describe how all ethnic groups develop “folk theories of success” in response to their historical experiences. When groups are excluded from access to success, caste-like experiences emerge and an oppositional identity is developed where institutions such as schools are viewed solely as expressions of mainstream culture. Under those conditions, it should come as no surprise to find high dropout levels, disaffection from tracks that may be more demanding, and reports on inner-city African Americans, suggesting that youth from these groups tend to value peer-related competencies more than school-related ones. Some peer-group cultures support negative attitudes toward school (Fordham and Ogbu 1986; Trimble 1996) choosing fun in the present over an uncertain job in the future (McLeod 1987). Achievement and performance. There are many ways of assessing achievement or performance (courses completed, grades obtained, test scores, etc.), and many factors can affect level of achievement (perceptions of usefulness, SES, etc.). Most studies of mathematics achievement have been limited to enrollment figures (CEEB 1986; Dix 1987; Marrett 1982; Matthews et al. 1984; Dossey et al. 1988; NLS 1980; NSF 1983–84; Sells 1980; Witthun 1982). According to this measure, progress has been made since 1982 not only in mathematics but also in the science fields. Latinos/ Latinas and African Americans take the beginning courses in both the mathematics (algebra 1) and science (biology) series in about equal proportions as whites (roughly 93 percent) but Latino and African American students still fall below white students (NCES 1995c:12). The proportion of high school students of all ethnicities taking physics and trigonometry has increased since 1982 (NCES 1995b, c:16). There are few gender differences in the number of mathematics courses taken by ethnic minority males and females in high school, but fewer American Indian and Mexican American women than men take calculus (Clewell and Ginorio 1996). Females of all underrepresented ethnic groups participated in science honors courses more often than males (Clewell and Ginorio 1996), even though fewer ethnic minority females than males had experiential science activities (Rakow and Walker 1985).

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High-ability students in each ethnic group were more likely to perceive mathematics as useful than low-ability students (Gross 1988). African Americans’ strong beliefs in the usefulness of mathematics both in the present and the future does not translate into higher performance in those courses; their performance seems to be closely related to perceived difficulty in doing mathematics. When looking at test scores rather than course grades, a gap in mathematics achievement exists between college-bound white and black seniors (Oakes 1990). But when controlling for the number of mathematics courses taken, most of the differences in test scores between African American and whites (Jones et al. 1986) and between Hispanic and white students disappear (Moore and Smith 1985, cited in Oakes 1990:50), as do the sex differences (Oakes 1990). Ekstrom, Goertz, and Rock (1988) reported that SES accounts for a large part of the variance in the mathematics achievement of college-bound students. Among high SES students, boys do better than girls in high school reading and mathematics, the difference being most striking among blacks. This latter finding is attributed to the fact that high SES black students are more likely to go to integrated schools, and black girls do not do as well as black boys in integrated schools. In addition, while a direct positive relation between SES and persistence in mathematics was reported by Lee and Ware (1986), African American high school girls were more likely to persist in mathematics regardless of SES. In another SES-related finding, Davis (1986, 1989) reports that while African American students take more mathematics courses in racially segregated schools, their mathematics achievement scores are lower in such schools. Secada (1991) contends that if the function of SES were the same for all populations, the model for achievement would be consistent across all groups, and therefore there would be a steady increase in achievement based on increasing SES. But achievement, participation, and eventual entrance into postsecondary careers in SME are not the same across all SES populations. Ogbu (1985) has argued that academic competencies are irrelevant to cultural imperatives among inner-city blacks. Fordam and Ogbu (1986) said that African Americans underachieve because they consider academic achievement “acting white.” This interpretation is not unreasonable, given the function of schools in transmitting not only information but also values; some have even argued that the transmission of values has been the primary function of schooling (Wallace 1990). Ethnic minority adolescents clearly understand the evaluations of their group by the majority culture (Spencer and Dornbusch 1990), and these evaluations affect their identity, their self-concept, and their educational orientation. Inner-city blacks also perceive the job ceiling they are likely to encounter even if they are well educated (Fordham and Ogbu 1986; McLeod 1987; NCES 1995b; Ogbu 1996). Given the low potential return, they may choose not to continue their education. Or those that choose college may choose careers that require only a bachelor’s degree, eliminating most academic careers and virtually all science and mathematics careers. Some students may see the world the way Fordham and Ogbu describe it, finding ways of staying in high school despite discrimination, especially if they find adults who emphasize the importance of education and the students’ ability to succeed (Levine and Nidiffer 1996). For those who stay in high school, Pollard (1994) reported that their academic self-concept was tied to achievement regardless of gen-

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der, and that urban African American girls had higher achievement levels than boys across all disciplines, a pattern for ethnic minority females that is different from that of white females. Thus, when measuring achievement or performance, it is necessary to be specific about conditions such as geographical location, levels of SES, proportion of students of different ethnicities in the school, and academic histories of the students. As Ceci and Hembrooke (1995) demonstrated in their review of performance in academic versus nonacademic mathematics, cognitive functioning is more contextspecific than has been assumed heretofore, even among high-level functioning adults. They have argued that in order to understand cognitive functioning, we need to be specific about the context in which cognition occurred. This is good advice not only for carrying out and interpreting research but also for developing curricula and intervention programs. Community and family. Family and community are considered very important for the academic achievement of all students (Entwisle 1990; Levine and Nidiffer 1996). As Entwisle writes, “Families provide models for students, intentional or unintentional instruction, home environments that can favor or deter academic pursuits, and financial resources that can encourage or discourage staying in school” (219). What is missing is the understanding of how families do that. Slaughter-Defoe and colleagues (1990) have demonstrated that family factors do not consistently predict functioning or achievement socialization across all ethnic groups. An example they give is that compared to African Americans, Hispanics tend to be in two-parent households but are less likely to finish high school than blacks. Perhaps that is because Latinos are expected to provide more financial and emotional support in their homes at an earlier age (Suárez-Orozco, cited in Patthey-Chavez 1993:46). Similarly, while authoritarian parenting styles lead to lower grades in school for most groups of students, this is not true of Asian American students (Dornbusch et al. 1987; Spencer and Dornbusch 1990) or African American students (Steinberg et al. 1995). We know very little about how discontinuities between school and home affect achievement (Gándara 1995; Vásquez-Nuttall and Romero-García 1989). One of the places where the parents’ role has been documented is in the decision to go to college (Antony 1996; hooks 1993; Langston 1993; Levine and Nidiffer 1996; Rodriguez 1993; Tokarczyk and Fay 1993). A high percentage of parents of both whites and Latinos encourage their high-school children to pursue college: 84 percent of white and 81 percent of Latina mothers, 78 percent of white and 75 percent of Latino fathers (NCES 1995:13). Urban parents were more supportive of their children attending college than were rural parents (Haas 1992). Students need money, information and encouragement in their pursuit of college. The high school graduation rates of students from families with annual incomes of less than $21,000 have hovered around 65 percent since 1970, while those of families with annual incomes of $39,000 and above have hovered at the 90 percent level (Mortenson 1995). Parents who do not have much education themselves cannot provide the kind of informal knowledge that could make a difference in their children’s long-term education. Among 1989 college freshman, 74 percent of Afri-

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can American students and 57 percent of white students had fathers who had not graduated from college (Astin 1990). First-generation college-bound students depend on the school’s advice to a greater extent than students whose families have greater human and cultural capital. Schools It is not only individual students’ characteristics that affect academic outcomes but also the procedures and makeup of the schools they attend, including tracking practices, composition of the student body, SES of students, and number and quality of the staff. Tracking, ability-grouping, and curriculum. Sorting students into groups is one of the most pervasive functions of schools. The basis for the sorting varies, depending upon who does the sorting and what purposes it serves. For ethnic minority students, some of the sorting is intentional and some incidental to other practices; some of it is by choice and some imposed by the school. Two of the groupings that have implications for the achievement of students and the pursuit of SME-related courses are ability-based grouping and tracking. Ability-based grouping (such as remedial or honors courses) increases as the students progress through school, although there has been a decrease of ability-based grouping at all levels from 1986 to 1993 in science and mathematics classes (Suter 1996). Groupings that lead to homogeneous classes may result in decreased opportunities for the low-achieving students to experience demanding coursework and receive encouragement to achieve. As Steele (1992)—echoing Treisman (1992)—has stated, “remediation defeats, challenge strengthens” (78). In those schools with mixed populations, black and Hispanic students are overrepresented in the slow tracks (Davenport 1993; Hansen, Walker, and Flom 1995; Oakes 1985), although Gamoran and Mare (1989) have argued that the assignment process favors blacks and females. In schools with high ethnic minority enrollment, a higher proportion of classes are slow track—unnecessarily so in the opinion of some of the teachers (Fordham 1996). But as Treisman’s work at the college level and experiments such as the one carried out at the Muirlands Middle School in La Jolla, California, demonstrate, the expectation that all students in a school perform as “scholars” or as “gifted students” leads to higher achievement by every student (Hansen, Walker, and Flom 1995; Treisman 1992). Tracking is based on actual or presumed abilities and aptitudes of students. Many schools that do not have an official tracking system do track students in practice, based on teachers’ and counselors’ evaluations of the students’ skills (Oakes 1985) and the capacity of the student or the students’ parents to understand the system enough to be able to appeal any unfavorable decisions. In principle, in those schools with tracking, equally high academic standards are expected in both the academic and vocational tracks. In practice, many vocational programs are considered suitable for those students who are judged to be less qualified. It has been reported that students with similar aptitudes show greater differences in achievement after being placed in different tracks (Gamoran 1986, cited in Oakes 1990:47).

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The curriculum in the schools can make the path to SME preparation harder for those ethnic minority students who persist in high school. Ekstrom and colleagues (1988) provided evidence that schools with high proportions of ethnic minority and/ or poor students offer fewer advanced courses. Furthermore, students in low SES schools who are in the academic track take fewer academic classes than students in higher SES schools (Rock, Braun, and Rosenbaum 1985). A disproportionate number of ethnic minorities enroll in vocational education or a nonacademic curriculum (Ekstrom, Goertz, and Rock 1988). Black students are underrepresented in college preparatory science courses (Wavering and Watson 1987), and those who are college bound are not likely to have the proper number or level of biological or physical science courses (Anderson 1989). Lee and Bryk (1988) have concluded that, independent of other factors, course and curriculum program assignments have a decisive effect on achievement and aspirations. In addition, the structure of the courses also has a significant effect. Slavin (1985) found that blacks and Latinos/ Latinas do better in cooperative or small learning groups; and Peterson and Fennema (1985) reported that hands-on, cooperative techniques are more helpful to girls and ethnic minorities in the area of mathematics achievement. In Kohn’s (1994) words: [S]tudents do not come to believe they are important, valued, and capable just because they are told that this is so or made to recite it. . . . Students acquire a sense of significance from doing significant things, from being active participants in their own education. (282)

Teachers and counselors. Teachers and counselors can make a difference for students. In order to be effective, teachers and counselors need to have proper credentials, access to the necessary resources, and to be responsive not only to cognitive tasks but also to the developmental and sociocultural needs of their students. Nationwide, only 72 percent of science high school teachers and 63 percent of mathematics high school teachers majored in their respective fields (Weiss, Matti, and Smith 1994, cited in Suter 1996). Teachers in inner-city schools serving mostly ethnic minority populations are more likely to be misassigned (for example, nonscience teachers teaching science courses) or to be holding provisional credentials than are teachers in other kinds of schools, and are consequently likely to move to another school. Teachers in schools with large numbers of low SES students also work with fewer resources, such as microcomputers (Becker 1983, cited in Oakes 1990:43). Teachers in high-track classes—which are less common in schools attended by ethnic minority students—teach differently: they spend more time on instructions, expect their students to do more homework, and do not use as much criticism (Oakes 1985). Behavior of teachers may vary in different contexts. Pollard (1993) reported that African American girls received more help in the classroom, while African American boys received more personal support outside the classroom. Teachers of the same ethnicity as the students may serve as role models, provide culturally sensitive ways of teaching, as well as instructions. Evans (1992) found that African American students whose mothers lacked a college education showed an 18 percent increase in achievement when they had African American teachers. This finding is contradicted in a report based on the NELS 1988 assessment, which found

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that the race, gender, and ethnicity of teachers does not affect learning by students in four subject areas but does affect some teachers’ evaluations of the students’ performance (Ehrenberg, Goldhaber, and Brewer 1995). For students who are already alienated from traditional educational achievements (as has been reported for African Americans; see Fordham 1996), the presence of same-ethnicity teachers is not a guarantee of achievement. Further, teachers also respond to the students’ ethnicity. Patthey-Chavez (1993) found that teachers’ ranking system of students in an innercity Los Angeles school was based on ethnicity, with Asian Americans being perceived as the most motivated and cooperative, foreign-born Latinos next, and local Latinos viewed as difficult to work with. Counselors play crucial roles in two areas that affect preparation for SME careers: tracking students through assignment to courses and post–high school planning, including college applications and information about financial aid. Counselors’ heavy loads adversely affect their performance of these tasks, and their administrative and disciplinary duties siphon off time from their advocacy roles (Hansen, Walker, and Flom 1995). Even though 65% of both whites and Latinos reported that teachers and guidance counselors encouraged them to go to college, the poorest students, who are likely to be first-generation college bound, report the least access to information from guidance counselors (CEEB 1986, cited in Oakes 1990:45); these students are consequently more affected by school deficiencies in the area of counseling. According to Rendón (1993), “students are deciding in high school what to do with their lives with very little guidance and support . . .” (9). For these students the absence of concrete information about how necessary preparation in high school can lead to college admission could prove to be the crucial missing link to their future success. Teachers, counselors, parents, as well as peers and other adults in the students’ community are part of a network that can engage the student in “reciprocal . . . enduring and . . . progressively more complex forms of behavior” (Ceci and Hembrooke 1995:329). These interactions, labeled proximal processes by Bronfenbrenner, are the engines that drive intellectual development (Ceci and Hembrooke 1995) and that prove essential for access to college (Levine and Nidiffer 1996). Individual teachers (and counselors) with passion and commitment can make a huge difference, as was exemplified by Jaime Escalante’s work with calculus at Garfield High School in east Los Angeles. Ultimately teachers and counselors in schools cannot be considered apart from the larger society that shapes their own as well as their students’ perceptions and experiences. Thus, the issues surrounding teachers and counselors cannot be addressed in isolation, divorced from such larger societal issues as tax funding for schools or society’s expectations of students of color who aspire to be scientists, mathematicians, or engineers. Summary The relationship between gender and interest in mathematics is mediated by race/ ethnicity. The evidence regarding attitudes of ethnic minority students toward science is mixed, with some studies suggesting higher levels of interest than whites and others suggesting lower levels. While this may be due to methodological issues, minority students’ attitudes toward education may affect their interest, motivation, and

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achievement in science and mathematics. As the preceding review of research demonstrates, the choice of college and SME is not dependent solely on the students’ interests and aptitudes. It requires the support of parents, teachers, and counselors, and can be facilitated by a college-bound peer group. This survey also shows that structural factors in schools, such as tracking or use of computers in different curricula, can derail those students whose families or communities do not have the human or capital resources to sustain the students’ ambitions.

Choosing Science, Mathematics, or Engineering Means Choosing College Choosing college as an educational option is officially done in high school, but some students and families choose college much earlier. Among entomologists, Pearson (1992) reported that ethnic minorities chose that career at an average age of eighteen, while whites chose it close to age fifteen. A three-year delay in making that choice may prove costly for ethnic minority students because choosing college is a daily decision at the high school level. Students choose college when they register for academic or college preparatory courses as they enter high school, when they participate in extracurricular activities, and when they do extra homework (Astin 1990). The selection of SME involves making specific choices within the curriculum; students choose SME when they continue to take mathematics courses beyond those required by their local schools for graduation, as well as when they choose to take all the science courses offered in their high school. They choose SME when they participate in Mathematics Engineering Science Achievement (MESA), Expanding Your Horizons, or any of the programs available to them in their schools or communities. They choose SME when they have a role model or mentor who encourages them to pursue that option (Levine and Nidiffer 1996; Etzkowitz et al. 1992). They choose SME when they can see themselves as scientists, engineers, or mathematicians. The cumulative effect on vocational options of each previous academically related decision becomes magnified as the remaining time in high school comes to an end. Extracurricular enrichment opportunities—essential for the more competitive colleges—are often unavailable to ethnic minority students as a result of the school’s limited offerings or the students’ own home or work commitments. Many of today’s students work in order to pay for their own and their family’s sustenance. The 50 percent of students who plan to go to college full time (Haas 1992) may see these jobs as opportunities to save money to pay for college. The 10 percent of students planning to work full time right after high school graduation may see these jobs as the beginning of their paid participation in the workforce. For the 25 percent of students who plan to work and attend college at the same time, holding down a job while studying is just the way it must be. If these jobs are related to their long-term vocational goals, they enrich the students intellectually as well as economically. Unfortunately, most jobs open to high school students are in the service sector and are unrelated to careers that require a college degree, including SME careers.

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Even though boys do better in tests than girls (AAUW 1992; NCES 1995d), girls are more likely to express interest in college and are more likely to attend college immediately after high school. High school senior boys are more interested in work or the military than college. Black males and, to a lesser extent, Latinos are interested in the military; these findings hold true even when controlling for SES or test scores. Hrabowski and Pearson (1993) cite the following as reasons for African American males not attending college: lack of preparation; family income; the influence of friends with no academic goals; and not being aware of the benefits of going to college. The aspirations to go to college translate into choices of courses or tracks in high school. Faced with choices about which track to choose, African Americans generally opt for the vocational track, while Asian Americans generally opt for the college preparatory track. About half of the Latinos in the Los Angeles high school studied by Patthey-Chavez (1993) had educational aspirations beyond high school, but only those in the college preparatory track were knowledgeable enough about postsecondary education to understand how their present education fit into their college plans. Students in effective schools or who have knowledgeable parents make academic decisions with the future in mind. Students in ineffective schools or whose parents are not familiar with the educational system are more likely to make poorly informed decisions or to let the tracking system in their schools make decisions for them. Intervention Programs If schools, as presently established, were doing a good job of educating all students in mathematics and science, the existing programs to encourage student interest in SME fields would not be necessary. Similarly, if schools were equally successful with all students, or at least with a representative sample of all students, programs targeted to underrepresented ethnic minority students and other groups would not be needed. A review of the literature indicates that although intervention programs for SME continue to multiply, not many of them focus on ethnic minority students. Of the NSF-supported Young Scholars Projects in 1990–91, only 25 out of 131 projects (roughly 19 percent) placed emphasis on the participation of ethnic minorities (NSF 1990). This figure is low if ethnic minorities are already underrepresented in this area, especially if they are predicted to become an increasing part of the job market through their proportionate increase in population. Many of the SME intervention programs assume some awareness on the part of students of career opportunities; thus, they are aimed at students who are already interested. Such programs ignore students who are more tentative about their goals, or about the way they express them, but whose interest might actually be piqued by some exposure to SME careers. Intervention programs may be particularly critical for ethnic minority students who are concentrated in low-achieving tracks or who come from families with no college graduates. Oakes (1985) notes that “through the selection and allocation system within schools and the differential education treatments students receive, schools either reinforce or modify students’ self-concepts and aspirations, so that they view their current and easily predicted social-class roles as

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appropriate” (145). Ethnic minority students selected to participate in an SME oriented program may have a chance to break out of the often inflexible tracking system in her or his school (Naidoo, personal communication, August 1996). Consequently, the eligibility requirements and the recruitment procedures of intervention programs should not duplicate the tracking systems in effect in most schools. Intervention programs tend to be of short duration. Of the twenty-five projects mentioned previously, twenty-one of them are held only during the summer and none offer any continuity for students throughout high school. When undertaken, evaluations for short-term programs usually consist of pre- and post-assessments, which by necessity cannot address the long-term effects of a short intervention. While such evaluations do show some immediate effects (Rook 1996), we have only sporadic evidence of their long-term effectiveness. Intervention programs have different formats; most are distinguished by strong academic components. Formats include (after-school or residential) research internships, provision of role models of the same sex or ethnicity/race, mentoring programs, and basic skill development in reading, writing, and mathematics. At present, intervention programs that largely originate outside schools are seen as a means of carrying out a large portion of the SME educational agenda. Programs aimed at individual students outside the school setting (such as the long-running Saturday Science Academy at Atlanta University, Operation SMART, and Expanding Your Horizons, sponsored by AAUW, Clewell, Anderson, and Thorpe 1992) will last as long as the need exists and outside support is offered. These individually oriented programs can be institutionalized at the community level, but because they do not involve the principal, teachers, or counselors, they do not change the systems operating within the schools. Programs that operate through the schools have a higher potential for reaching all students in the school but require a greater investment of energy in gaining support from the school and occasionally from the community. Unless intervention programs that involve the school find ways of providing opportunities that expand and challenge the students in ways that complement the school’s programs, they will be resisted. Students are likely to be drawn to the challenge offered by almost all intervention programs. However, the academic challenge is not the only one likely to face ethnic minority students. In light of the previous findings on attitudes and perceptions of SME by ethnic minority students, as well as influences affecting their achievement and performance, effective intervention programs in high school must address not only the academic component but also the need for students to develop a sense of autonomy in the context of their community (parents as well as peers). They must also focus attention on the financial constraints faced by the student. In a study of Mexican American students, Rendón (1985) suggested that one of the components should include parents, a suggestion that should be followed with other racial/ethnic groups for whom parents are also influential (Seymour and Hewitt 1996). In addition, there has to be a sense of community between students and staff, and academic links must be forged between peers. Thus, a peer group’s importance is not denied but rather reshaped in order to become a positive force supporting academic goals.

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Of all the SME programs reviewed, the most surprising omission was the lack of any link to the developmental process that adolescents are themselves undergoing in high school. Few of these programs indicate how the personal aspects of the process as they relate to the individual student will be accommodated. This exclusion has profound consequences, since choosing SME as a career is tied to the central developmental tasks of creating a self-identity and increasing one’s autonomy. But if the ties are not obvious to the program creators, they cannot be made explicit to the students. Nevertheless, not a single program was found that explicitly addressed and/or accommodated this developmental process as part of the intervention strategy. For ethnic minority students this process is likely to be conflated with the realization of the stigma attached to race/ethnicity in the United States. And for poor students a similar but more subtle process occurs around his or her SES. A recent report published by AAUW (Hansen, Walker, and Flom 1995) focuses on programs that work for girls and emphasizes the importance of five action steps—celebrate girls’ strong identity; respect girls as central players; connect girls to caring adults; ensure girls’ participation and success; and empower girls to realize their dreams—that ensure a girl’s success in academic tasks. These principles are also likely to hold for minority students, who might need more personalized counseling and moral support. The value of intervention programs lies in their rapid response to the problem. Ultimately, however, their success will be measured by the achievement of the programs’ long-term goals and the degree to which they have been institutionalized. In this regard, many of the programs appear to be less than successful. There now exists a substantial body of work on what constitutes the key elements and organizational components of such interventions (Clewell, Anderson, and Thorpe 1992; Davis and Rosser 1996). However, we need to understand why some strategies may be more beneficial than others amongst the various groups. There is still a need for studies on how these programs may be tailored to better suit the needs of different groups whether these be girls (Clewell, Anderson, and Thorpe 1992; Hansen, Walker, and Flom 1995), ethnic minority students, or students in rural communities. Many programs have not been evaluated, and for those evaluations that do exist the results are often difficult to locate. The dissemination of this information is essential, particularly for those who will be creating new programs or improving existing programs. Programs are begun by people in different disciplines (science, mathematics, engineering, education, psychology) and in various locations (industry, academia, school districts, community groups) and regions across the country. This dispersion makes it critical that this information be shared among the various populations. Thus, the time is ripe to “pilot new means of distribution using approaches such as electronic media and clearinghouses” (Hollenshead et al. 1996: 326). Summary If schools and programs succeed in maintaining or developing a high school student’s interest in SME fields, the student will have to make a decision as to whether to seek

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admission into a college. This decision is sustained on a daily basis. Ethnic minority students make the official decision to go to college later than whites and often have less access to resources. Intervention programs are seen as complementing and, in some cases, filling the void left by schools in support of SME education. Most of these programs are academically strong but short on support or on linking the components of the program to developmental tasks. Many programs also lack ties to the schools or to the community, thus making institutionalization of accomplishments difficult. Few SME intervention programs are evaluated and dissemination of that information is cumbersome. Our focus on students from the eighth grade through the last year of high school acknowledges factors that shape the SME experiences of populations underrepresented in these fields. Needless to say, any student who is not equipped with the requisite SME courses will be barred from careers that require mathematics and a knowledge of technological tools. Ethnic minority students are overrepresented among those who lack these tools. Students, families, schools, communities, and society at large all have a part to play. Students need to be made aware of the opportunities for careers in science and how these are tied to high school courses in science and mathematics as well as extracurricular activities. Families need to support, to the best of their abilities, their children’s academic involvement in high school. Families confronting basic economic necessities may choose short-term resources over long-term potential gains if they are not aware of currently available financial resources to make the path from high school to college possible for their children. Communities must address their own negative expectations of subsets of students if the benefits from school–community partnerships are to be equally distributed across all groups of students. Society must put the education of all its children at the top of its list of priorities and support schools fully so that teachers and counselors can provide the best environment within which students can decide what they want to be when they grow up. Much research has already been done in some of these areas, but much of the research is flawed in that it ignores regional differences and SES disparities as well as gender differences. There is also not enough of an acknowledgment of how developmental tasks of middle adolescence and issues of stigma for ethnic minorities are tied to academic work in SME. Generalizations drawn from research that is based on a unidimensional or noncontextualized design can lead to major mistakes in practice. There is no single formula in the design of curricula or programs that will lead students to choose SME because there are many variations based on the following factors: student characteristics such as gender, ethnicity, or generational status; school characteristics such as the proportion of students within various SES, reliance on tracking for groupings, as well as the strength of the faculty and counselors; and community characteristics. However, a number of principles should be considered in the development of curricula or intervention programs designed to increase interest and sustain persistence on SME-related areas. 1. In order for students to commit to the more demanding curriculum required for a solid college preparation in the sciences, the students as well as their families

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and peers need to understand the usefulness of the endeavor not only for the long term but also for the near future. Since the path from student in high school to SME graduate may seem long and arduous, it is necessary to explain how the student can accomplish this and what rewards await the student as each step is attained. 2. In addition to monetary rewards, usefulness is reinforced when the curricula or programs respect and validate the values of the students and their families. Thus, they need to see how a career in SME can be integrated with their cultural or family values. While students will expand and change some of their values as they mature, this should not be a prerequisite for the pursuit of SME. 3. Curricula or programs need to acknowledge the developmental tasks that occupy the students as they develop the skills necessary to pursue SME. 4. Expectations of success on the part of the student must be coupled with the appropriate level of support to challenge and validate the student’s abilities. 5. Groupings that are diverse, offer hands-on experiences, and keep students focused on a given task have proved successful in a number of different settings. 6. Each student needs at least one person to serve as a mentor, someone who has faith in them and will provide necessary information or support at key junctures involving choice. Adolescents who have the opportunity to participate in curricula or programs that include these concerns will feel supported in their endeavors. Those who have the support of caring people can look to the future with hope. Adolescents who are not strained by current economic needs have the luxury of considering which, among various potential selves, they imagine they may choose. Only then can they tackle the issue of what they want to be when they grow up—and if family, school, and community have done their job well, many of them will choose to be scientists, mathematicians, or engineers.

References American Association of University Women (AAUW) Educational Foundation. 1992. How schools shortchange girls. Washington, DC: AAUW. Anderson, B. 1989. Black participation and performance in high school science. In W. Pearson Jr. and H. K. Bechtel, eds., Blacks, science, and American education, 43–58. New Brunswick, NJ: Rutgers University Press. Antony, J. J., Jr. 1996. Factors influencing college students’ abandonment of medical career aspirations. Unpublished doctoral dissertation. University of California, Los Angeles. Armstrong, J. M., and R. A. Price. 1982. Correlates and predictors of women’s mathematics participation. Journal for Research in Mathematics Education 13(2): 99–109. Astin, A. W. 1990. The black undergraduate: Current status and trends in the characteristics of freshmen (Research Report). Los Angeles: University of California, Graduate School of Education, Higher Education Research Institute. Bear, G. G., H. C. Richards, and P. Lancaster. 1987. Attitudes toward computers: Validation of a computer attitudes scales. Journal of Educational Computing Research 3(2): 207– 218. Becker, H. J. 1983. School uses of microcomputers: Reports from a national survey. Baltimore, MD: Johns Hopkins University Center for the Social Organization of Schools.

168 Adolescence Onward Berryman, S. F. 1983. Who will do science? Trends and their causes in minority and female representation among holders of advanced degrees in science and mathematics. New York: Rockefeller Foundation. Campbell, N. J., and K. M. Perry. 1988. Sex and ethnic group differences in high school students computer attitudes and computer attributions. ERIC Document Reproduction Service no. ED 37859. Ceci, S. J., and H. A. Hembrooke. 1995. A bioecological model of intellectual development. In P. Moen, G. H. Elder Jr., and K. Lüscher, eds., Examining lives in context: Perspectives on the ecology of human development, 303–346. Washington, DC: American Psychological Association. Clewell, B. C., and B. T. Anderson. 1991. Women of color in mathematics, science, and engineering: A review of the literature. Washington, DC: Center for Women Policy Studies. Clewell, B. C., B. T. Anderson, and M. Thorpe. 1992. Breaking the barriers: Helping female and minority students succeed in mathematics and science. San Francisco: Jossey-Bass. Clewell, B. C., and A. B. Ginorio. 1996. Examining women’s progress in the sciences from the perspective of diversity. In C. S. Davis, A. B. Ginorio, C. S. Hollenshead, B. B. Lazarus, and P. M. Rayman, eds., The equity equation: Fostering the advancement of women in the sciences, mathematics, and engineering, 163–231. San Francisco: Jossey-Bass. College Entrance and Examination Board (CEEB). 1986. Keeping options open. New York: College Entrance and Examination Board. Davenport, L. R. 1993. The effects of homogeneous groupings in mathematics. ERIC Document Reproduction Service no. ED 359065. Davis, C. and S. V. Rosser. 1996. Program and curricular interventions. In C. S. Davis, A. B. Ginorio, C. S. Hollenshead, B. B. Lazarus, and P. M. Rayman, eds., The equity equation: Fostering the advancement of women in the sciences, mathematics, and engineering, 232–259. San Francisco: Jossey-Bass. Davis, J. D. 1986. The effect of mathematics course enrollment on racial/ethnic differences in secondary school mathematics achievement. NAEP report no. 86-EMC. Princeton, NJ: Educational Testing Service. ———. 1989. The mathematics education of black high school students. In W. Pearson, Jr., and H. K. Betchel, eds., Blacks, science and american education, 23–42. New Brunswick, NJ: Rutgers University Press. Dix, L. S., ed. 1987. Minorities: Their underrepresentation and career differentials in science and engineering. Washington, DC: National Academy Press. Dornbusch, S. M., P. L. Ritter, P. H. Leiderman, D. F. Roberts, and M. J. Fraleigh. 1987. The relation of parenting style to adolescent school performance. Child Development 58: 1244–1257. Dossey, J. A., I. V. S. Mullis, M. M. Lindquist, and D. L. Chambers. 1988. The mathematics report card: Are we measuring up? Trends and achievement based on the 1986 national assessment (Report No. 17–M–01). Princeton, NJ: The Nation’s Report Card, NAEP, Educational Testing Service. Eccles, J. S. 1994. Understanding women’s education and occupational choices. Psychology of Women Quarterly 18, 585–509. Eccles, J. S., D. MacIver, and L. Lange. 1986. Classroom practices and motivation to study math. Paper presented at the annual meeting of the American Educational Research Association, San Francisco. Ehrenberg, R. G., D. D. Goldhaber, and D. J. Brewer. 1995. Do teachers’ race, gender and ethnicity matter? Evidence for the national educational longitudinal study of 1988. Industrial and Labor Relations Review 48(3): 547–561.

The Transition to and from High School of Ethnic Minority Students

169

Ekstrom, R. B., M. E. Goertz, and D. Rock. 1988. Education and American youth. Philadelphia, PA: Falmer Press. Entwisle, D. R. 1990. Schools and adolescence. In S. S. Feldman and G. R. Elliot, eds., At the threshold: The developing adolescent, 197–224. Cambridge, MA: Harvard University Press. Etzkowitz, H., C. Kemelgor, M. Neuschatz, and B. Uzzi. 1992. Athena unbound: barriers to women in academic science and engineering. Science and Public Policy 19(3): 157–179. Evans, M. O. 1992. An estimate of race and gender role-model in teaching high school. Journal of Economic Education 23(3): 209–217. Feldman, S. S., and G. R. Elliot, eds. 1990. At the threshold: The developing adolescent. Cambridge, MA: Harvard University Press. Fennema, E., and J. Sherman. 1976. Sex-related differences in mathematics learning: Myths, realities and related factors. Paper presented at the annual meeting of the American Association of the Advancement of Science, Boston. Fordham, S. 1996. Blacked out: Dilemmas of race, identity and success at Capital High. Chicago: University of Chicago Press. Fordham, S., and J. Ogbu. 1986. Black students’ school success: Coping with the burden of acting white. Urban Review 18: 176–206. Gamoran, A. 1986. The stratification of high school learning opportunities. Paper presented at the annual meeting of the American Educational Research Association, San Francisco. Gamoran, A., and R. D. Mare. 1989. Secondary school tracking and educational inequality: Compensation, reinforcement or neutrality? American Journal of Sociology 94: 1146– 1183. Gándara, P. 1995. Over the ivy walls: The educational mobility of low-income Chicanos. Albany: SUNY Press. Ginorio, A. B. 1995. A culture of meaningful community. Paper presented at the Bridging the Gender Gap conference sponsored by Intel Corporation, Carnegie Mellon University. Grandy, J. 1987. Ten-year trends in SAT scores and other characteristics of high school seniors taking the SAT and planning to study mathematics, science or engineering: Research Report. Princeton, NJ: Educational Testing Service. Grignon, J. R. 1993. Computer experience of Menominee Indian students: Gender differences in coursework and use of software. Journal of American Indian Education 32(3): 1–15. Gross, S. 1988. Participation and performance of women and minorities in mathematics, Vol. 1: Findings by gender and racial/ethnic group. Rockville, MD: Montgomery County Public Schools, Carver Educational Services Center. Haas, T. 1992. What can I become? Educational aspirations of students in rural America. Charleston, WV: Appalachia Educational Laboratory. ERIC/CRESS Digest EDO–RC– 91–11. Handel, R. D. 1986. Achievement attitudes in mathematics and science: Relationships between self-perceptions, aspirations and extra-curricular activities. Paper presented at the annual meeting of the American Educational Research Association, San Francisco. Hansen, S., J. Walker, and B. Flom. 1995. Growing smart: What’s working for girls in school. Washington, DC: American Association of University Women Educational Foundation. Harter, S. 1990. Adolescent self and identity development. In S. S. Feldman and G. R. Elliot, eds., At the threshold: The developing adolescent, 352–387. Cambridge, MA: Harvard University Press.

170 Adolescence Onward Hill, O. W., W. C. Pettus, and B. A. Hedin. 1990. Three studies of factors affecting the attitudes of blacks and females toward the pursuit of science and science-related careers. Journal of Research in Science Teaching 27(4): 289–314. Hollenshead, C. S., S. A. Wenzel, M. N. Dykens, C. Davis, A. B. Ginorio, B. B. Lazarus, and P. M. Rayman. 1996. Ensuring educational and career equity for women: A research and policy agenda. In C. S. Davis, A. B. Ginorio, C. S. Hollenshead, B. B. Lazarus, and P. M. Rayman, eds., The equity equation: Fostering the advancement of women in the sciences, mathematics, and engineering, 321–333. San Francisco: JosseyBass. hooks, b. 1993. Keeping close to home: Class and education. In M. M. Tokarczyk and E. A. Fay, eds., Working-class women in the academy. Amherst: University of Massachusetts Press. Howe, A. C. 1982. Classroom process variables in urban integrated junior high school individualized science programs: Final report. Syracuse, NY: Syracuse University. ERIC ED 220283. Hrabowski, F. A., III, and W. Pearson, Jr. 1993. Recruiting and retaining talented African American males in college science and engineering. Journal of College Science Teaching 22(4): 234–238. Hueftle, S. J., S. J. Rackow, and W. W. Welch. 1983. Images of science: A summary of results from the 1981–1982 national assessment in science. Minneapolis: University of Minnesota. James, R. K., and S. Smith. 1985. Alienation of students from science in grades 4–12. Science Education 69(1): 39–45. Jones, L. V., E. C. Davenport, A. Bryson, T. Bekhuis, and R. Zwick. 1986. Mathematics and science test scores as related to courses taken in high school and other factors. Journal of Educational Measurement 23(3): 197–208. Jones, L. V., I. V. S. Mullis, S. A. Raizen, I. R. Weir, and E. A. Weston. 1992. The 1990 science report card: NAEP’s assessment of fourth, eighth and twelfth graders. Washington, DC: National Center for Education Statistics. Kahle, J. B. 1987. SCORES: A project for change? International Journal of Science Education 9: 325–333. ——— . 1994. The schooling of girls: Optimizing opportunities or obstacles? Paper presented at the Sloan Conference on Women in Science, Mathematics and Engineering. Wellesley College, Wellesley, MA. Kohn. 1994. The truth about self-esteem. Phi Delta Kappan 76(4): 272–283. Langston, D. 1993. Who am I now? The politics of class identity. In M. M. Tokarczyk and E. A. Fay, eds., Working-class women in the academy. Amherst: University of Massachusetts Press. Lantz, A. E., and G. P. Smith. 1981. Factors influencing the choice of nonrequired mathematics courses. Journal of Educational Psychology 73: 825–837. Lee, V. E., and A. S. Bryk. 1988. Curriculum tracking as mediating the social distribution of high school achievement. Sociology of Education 62: 78–94. Lee, V. E., and N. C. Ware. 1986. When and why girls “leak” out of high school mathematics: A closer look. Paper presented at the annual meeting of the American Educational Research Association, San Francisco. Levine, A., and J. Nidiffer. 1996. Beating the odds: How the poor get to college. San Francisco: Jossey-Bass. Marrett, C. B. 1982. Minority females in high school mathematics and science. Washington, DC: National Institute of Education. McLeod, J. 1987. Ain’t no making it: Leveled aspirations in low-income neighborhoods. Boulder, CO: Westview Press.

The Transition to and from High School of Ethnic Minority Students

171

Meyer, M., and M. S. Koehler. 1990. Internal influences on gender differences in mathematics. In E. Fennema and G. C. Leder, eds., Mathematics and gender. New York: Teachers College Press. Miura, I. T. 1987. Gender and socioeconomic status differences in computer interest and use. Paper presented at the annual meeting of the American Educational Research Association. Moore, E. G. J., and A. W. Smith. 1985. Mathematics aptitude: Effects of coursework, household language and ethnic differences. Urban Education 20: 273–294. Mortenson, T. G. 1995. Financing opportunity for postsecondary education. Paper presented at NACME/Sloan Conference on Ethnic Minorities in Science, Engineering, and Mathematics, Wake Forest, NC. Mullis, I. V. S., and L. B. Jenkins. 1988. The science report card: Trends and achievement based on the 1986 national assessment (Report No. 17–S–01). Princeton, NJ: Educational Testing Service. National Center for Education Statistics (NCES). 1990. The tested achievement of the National Education Longitudinal Survey of 1988 eighth grade class. Washington, DC: U.S. Department of Education. ———. 1995a. Findings from the condition of education, no. 1: High school students ten years after “A nation at risk”. NCES 95-764. Washington, DC: U. S. Department of Education. ——— . 1995b. Findings from the condition of education, no. 2: The educational progress of black students. NCES 95-765. Washington, DC: U.S. Department of Education. ——— . 1995c. The condition of education, no. 4: The educational progress of Hispanic students. NCES 95-767:80. Washington, DC: U.S. Department of Education. ——— . 1995d. Findings from the condition of education, no. 5: The educational progress of women. NCES 96-768. Washington, DC: U.S. Department of Education. NLS. 1980. Cited in AAUW, 1992. National Science Foundation (NSF). 1983–84. Directory of NSF-supported young scholars projects. NSF Publication No. 9046. Washington, DC: U.S. Government Printing Office. ——— . 1990. Women and minorities in science and engineering. NSF Publication No. 90301. Washington, DC: U. S. Government Printing Office. Oakes, J. 1985. Keeping track: How schools structure inequality. New Haven, CT: Yale University Press. ———. 1990. Lost talent: The underparticipation of women, minorities, and disabled persons in science (R–3774). Santa Monica, CA: Rand Corporation. Ogbu, J. 1985. Research currents: Cultural-ecological influences on minority school learning. Language Arts 62: 860–869. ——— . 1996. Minority status and education in the urban context. Paper presented at the annual meeting of the American Psychological Association, Toronto. Patthey-Chavez, G. G. 1993. High school as an arena for cultural conflict and acculturation for Latino Angelinos. Anthropology and Education Quarterly 24(1): 33–60. Pearson, G. A. 1992. A funny thing happened on the way to graduate school: Race, gender, and entomology career choice. American Entomologist 38(2): 80–83. Peterson, P. L., and E. Fennema. 1985. Effective teaching, student engagement in classroom activities, and sex-related differences in learning mathematics. American Educational Research Journal 22(3): 309–335. Pollard, D. S. 1994. Gender, achievement, and African American students’ perceptions of their school experience. Educational Psychologist 28(4): 341–356. Rackow, S. J., and C. L. Walker. 1985. The status of Hispanic American students in science: Achievement and exposure. Science Education 69: 557–565.

172 Adolescence Onward Rendón, L. I. 1993. Eyes on the prize: Students of color and the bachelor’s degree. Community College Review 21(2): 3–13. Rock, D., H. I. Braun, and P. R. Rosenbaum. 1985. Excellence in high school education: Cross-sectional study, 1980–1982 (Final Report). Princeton, NJ: Educational Testing Service. Rodriguez, C. M. 1993. Minorities in science and engineering: Patterns for success. Unpublished doctoral dissertation, University of Arizona. Rook, N. 1996. Attitudes to science and mathematics: The effects of a brief workshop for girls. Master’s thesis, University of Washington, Seattle. Secada, W. 1991. Evaluating the mathematics education of limited English-proficient students in a time of educational change. In Focus on evaluation and measurement. Vols. 1 and 2: Proceedings of the national research symposium on limited English-proficient student issues. Washington, DC. Sells, L. W. 1980. Mathematics: The invisible filter. Engineering Education 70(4): 340–341. Seymour, E., and N. M. Hewitt. 1996. Talking about leaving: Factors contributing to high attrition rates among science, mathematics, and engineering undergraduate majors: Final report to the Alfred P. Sloan Foundation on an ethnographic inquiry at seven institutions. Boulder, CO: Westview Press. Sherman, J. 1979. Predicting mathematics performance in high school girls and boys. Journal of Educational Psychology 71(2): 242–249. Slaughter-Defoe, D. T., K. Nakagawa, R. Takanishi, and D. J. Johnson. 1990. Toward cultural and ecological perspectives on schooling and achievement in African and Asian American children. Child Development 61: 363–383. Slavin, R. 1985. Cooperative learning: Applying contact theory in desegregated schools. Journal of Social Issues 41: 45–62. Spencer, M. B., and S. M. Dornbusch. 1990. Challenges in studying minority youth. In S. S. Feldman and G. R. Elliot, eds., At the threshold: The developing adolescent, 123– 146. Cambridge, MA: Harvard University Press. Steele, C. M. 1992. Race and the schooling of Black Americans. Atlantic Monthly 264(4): 68–78. Steinberg, L., N. E. Darling, A. C. Fletcher, B. B. Brown, and S. M. Dornbusch. 1995. Authoritative parenting and adolescent adjustment: An ecological journey. In P. Moen, G. H. Elder Jr., and K. Lüscher, eds., Examining lives in context: Perspectives on the ecology of human development, 423–466. Washington, DC: American Psychological Association. Suárez-Orozco, M. 1989. Central American refugees and U.S. high schools. Stanford: Stanford University Press. Suter, L. E., ed. 1996. The learning curve: What we are discovering about U.S. science and mathematics education (NSF96–53). Washington, DC: Division of Research, Evaluation and Communication; Directorate for Education and Human Resources; National Science Foundation. Thomas, G. E. 1986. The access and success of Blacks and Hispanics in U.S. graduate and professional education. Washington, DC: National Research Council. Tokarczyk, M. M., and E. A. Fay, eds. 1993. Working-class women in the academy: Laborers in the knowledge factory. Amherst: University of Massachusetts Press. Treisman, U. 1992. Studying students studying calculus: A look at the lives of minority mathematics students in college. College Mathematics Journal 23(5): 362–372. Trimble, J. 1995. Towards an understanding of ethnicity and ethnic identity and their relationship with drug use research. In G. Botwin, S. Schinke, and M. Orlandi, eds., Drug abuse prevention with multiethnic youth, 3–27. Thousand Oaks, CA: Sage.

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Vásquez-Nutall, E., and I. Romero-García. 1989. From home to school: Puerto Rican girls learn to be students in the United States. In C. T. García-Coll, and M. de L. Mattei, eds., The psychosocial development of Puerto Rican women. New York: Praeger. Visser, D. 1986. Sex differences in mathematics achievement and participation. Paper presented at the annual meeting of the American Educational Research Association, San Francisco. Von Destinon, M., B. Ganz, and M. Engs. 1993. Outcomes assessment and minority students in community colleges. Community College Journal of Research and Practice 17: 497–508. Wallace, M. 1990. Modernism, postmodernism, and the problem of the visual in Afro-American culture. In R. Ferguson, M. Gever, T. T. Minh-ha, and C. West, eds., Out there: Marginalization and contemporary cultures. Cambridge, MA: MIT Press. Wavering, M. J., and C. D. Watson. 1987. Survey to determine women and minority participation in mathematics and science programs in the public secondary schools of Arkansas. Report to the Arkansas Department of Education, Vocational and Technical Education Division. Little Rock, AR: Arkansas State Department of Education. Weiss, I. R., M. C. Matti, and P. S. Smith. 1994. Report of the 1993 national survey of science and mathematics education. Chapel Hill, NC: Horizon Research Inc. Witthun, J. 1982. Minnesota Indian girls and math: Final report (G008103969). Washington, DC: Women’s Educational Equity Act Program. Yager, R. E. 1993. Promising practices in high school science. Exemplary Practice Series, 605. Bloomington, Indiana: Center for Evaluating Development and Research, Phi Delta Kappa.

barbara schneider

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barbara schneider

Explaining the Unrealized Aspirations of Racial and Ethnic Minorities Understanding the factors affecting career choices requires examining both individual and situational conditions. (For a further explanation of this perspective, see the chapter by Ginorio and Grignon in this volume). Occupational choice is an evolving process influenced by individual factors such as gender, race and ethnicity, cognitive abilities, and the socializing contexts of family, peer groups, school, and community, all of which can change over time (Schneider 1994). Too often scholars have overlooked or underestmiated the potential impact that socializing contexts can have on determining the attitudes and motivations of young people, which eventuate in future life choices. This chapter will examine how individual and contextual factors affect young people’s educational expectations and occupational aspirations. Particular attention will be given to the role of the school in this process. Focusing primarily on school organizational practices such as sorting students, on the basis of ability, into various instructional groups, promotion policies, and course and program selections, this chapter will explore how these conditions can influence the educational expectations and career aspirations of youngsters in elementary and secondary schools. Specific attention will be paid to variations in the expectations and aspirations of students from different racial and ethnic minorities and socioeconomic classes. Narrowing the educational differences in achievement and attainment between economically advantaged white and disadvantaged minority students is a difficult task requiring complicated solutions. Thirty years of extensive research explicitly indicates the importance of family and community social resources in improving student learning (Coleman et al. 1966; Coleman 1987; Wilson 1987). Increasing incidences of family disorganization and the further isolation of poor communities have only exacerbated the plight of many poor and minority children. The grave disparities in family and community resources between economically advantaged white This chapter is partially funded by the Alfred P. Sloan Foundation, Study of Youth and Social Development, and the National Science Foundation (Grant #RED–9255880). The views expressed here are those of the author and do not represent those of the Alfred P. Sloan Foundation or the National Science Foundation.

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students and minority and poor students, which are present even before students enter elementary school, make the task of narrowing the achievement gap among different groups even more problematic. Entwisle and Alexander (1993) have shown that young minority children from severely economically disadvantaged homes and communities find the transition from home to early formal schooling especially challenging and difficult. The situation, however, is not hopeless. The expanding body of research on policy and instructional practices points to some key actions that schools, teachers, and parents can take to enhance educational achievement and attainment for all groups of students (Levin 1993). School organizational factors can have a powerful influence on altering student academic performance and educational expectations. For example, one of the most important school organizational policies proven to have a powerful independent effect on achievement and attainment, other than individual and family characteristics, is course and curriculum program assignment (Lee and Bryk 1988). School policies related to course selection, which start as early as middle school, seem to more directly influence achievement, attainment, and aspirations than other policies, such as minimum competency testing.

Restricting Educational Opportunities: Eighth-Grade Gatekeeping One of the most important findings of the 1980s was the recognition of the significance of the middle school experience and its lasting effects on students’ school careers (Clewell and Braddock in this volume; Oakes 1985; Oakes 1994; Carnegie Council on Adolescent Development 1990). Schooling opportunities in middle school were found to exert a significant influence on cognitive skills and later high school programmatic choices (Alexander and Cook 1982). Even though most students in middle school are required to take a series of basic courses in English, mathematics, science, and social studies, their school experiences are distinctly different. Despite the appearance of a fairly standard general curriculum, middle schools engage in the same type of sorting and grouping practices found in high schools that lead to inequitable learning opportunities. The practice of ability grouping and the assignment of students to advanced mathematics courses and foreignlanguage classes in the middle school has been shown to limit both present and future opportunities for learning, especially among minority and economically disadvantaged students. Ability grouping is discussed in detail in the preceding chapter by Ginorio and Grignon. Suffice it to say here that the controversial effects of ability grouping have prompted the major national professional mathematics and science associations to recommend that teachers restructure classrooms in order to avoid its use. Both the National Council of Teachers of Mathematics (NCTM) and the National Science Teachers Association (NSTA) have adopted standards and policies designed to encourage teachers to create learning environments that engage students who possess varying levels of ability. These professional groups have directed their attention to grouping practices in elementary and secondary schools.

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Course Selection Course selection in eighth grade is another form of curricular differentiation that appears to perform a major gatekeeping function for students making the transition into high school (Hallinan 1994; Oakes 1985; Smith 1995a and 1995b; Stevenson et al. 1994; Useem 1991). Which courses students take in eighth grade appears to affect high school course selection and test-score performance (Stevenson et al. 1994). For example, the mathematics curriculum, in contrast to other subject areas such as English and social studies, becomes sharply differentiated beginning in middle school (McKnight et al. 1987; Usiskin, 1987). Students given instruction in algebra rather than general mathematics in eighth grade are at an advantage both with respect to which courses they can take in high school and how fast they can move through the high school mathematics curriculum. Minorities and students from families with lower socioeconomic resources are less likely to take algebra in the eighth grade. It has been shown that this can have long-term consequences on future schooling careers. In a detailed analysis of the National Education Longitudinal Study of 1988 (NELS:88) involving twenty-four thousand students, Smith (1995a) found that students who took algebra in the eighth grade were more likely to have higher mathematics achievement scores and higher educational aspirations in the tenth grade. Although algebra may be the present “gatekeeper” for high school success, Smith hypothesizes that having everyone take algebra in the eighth grade may not lead to greater academic success for all students. She speculates that if everyone were expected to take algebra in the eighth grade, it is likely that another sorting mechanism would be introduced, such as more intractable ability grouping or the introduction of algebra in the seventh grade for high-achieving students. Presently minorities and students with limited social and economic resources are most affected by the current policy of offering algebra in the eighth grade to selected groups of students. Even if Smith is correct, in the short term more teachers and parents need to be made aware of the effect of eighth-grade course selection. In California, for example, middle school counselors encourage minority students who expect to go to college to take algebra in the eighth grade. The counselors also guide these minority students in the choice of their high school curricular program and course selection (Knauth, Schneider, and Makris 1995). Enrollment in advanced science courses in middle school does not have the same long-term effects as mathematics enrollment (Stevenson et al. 1994). The science courses students take in the eighth grade do not appear to affect later course selection in high school or test scores. This may be the case because, unlike mathematics, the science curriculum is less hierarchically structured. Knowledge and skills taught in one science course are not necessarily dependent on earlier courses at this level. Students can take biology before or after earth science, whereas geometry is nearly always required before taking trigonometry. Selecting a High School Program Before eighth graders enter high school, they are often given an opportunity to select a specific curricular program from among several options, commonly labeled col-

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lege preparatory, general or comprehensive, and vocational. These programmatic choices are not benign with respect to students’ schooling careers. Data from earlier National Center for Education Statistics studies such as High School and Beyond (HS & B), NELS:88, and other smaller scale studies have linked high school curricular placement to achievement, educational expectations, and occupational aspirations (see Oakes, Gamoran, and Page 1992; Gamoran and Mare 1989; Hotchkiss and Dorsten 1987). High school curricular program placement has also been shown to affect students’ friendships (Hallinan and Williams 1989) and self-esteem (Oakes 1985). However, less obvious connections have been found between socioeconomic status and curriculum placement (Natriello, Pallas, and Alexander 1989). There does appear to be a persistent link between race and ethnicity, on the one hand, and academic placement on the other. Minorities and students with fewer socioeconomic resources are more likely to be overrepresented in such lower curricular tracks as general and vocational. Analyses of the NELS:88 data show that by the spring of their eighth-grade year, nearly a quarter of eighth graders are uncertain about the high school program in which they plan to enroll (U.S. Department of Education, 1990). Hispanics and American Indians appear to be the most overrepresented within this group. Asians are significantly more likely to enroll in a college preparatory program. In contrast, African Americans are more likely than whites and Asians to enroll in a vocational program. Whites are less likely than Hispanics and African Americans to enroll in a vocational program. Given the fact that high school programmatic choice can be critical in influencing student school careers, it is clearly unsettling that nearly a fourth of the students do not know what program they plan to take in high school, and that those most uncertain about their plans are racial and ethnic minorities. The NELS:88 data highlight the variation in learning opportunities that many minority and low-income students face even before entering high school. Providing more detailed information about which curricular programs lead to college admission would greatly assist many students, especially those whose families have never attended postsecondary schools. It would also greatly reduce the uncertainty Hispanics and American Indians have regarding curricular placement and perhaps give them better information for planning their high school experiences.

High School Experiences Moving from middle school to high school can be a difficult and uncertain period (Schiller 1995; Smith 1995b). The transition process seems to be most problematic for ethnic and racial minorities living in urban areas. Using data from the NELS:88 base year and the first follow-up study, Schiller (1995) found that African Americans and Hispanics in urban areas with limited family resources were more likely to be uncertain about what high school they would attend and seemed to have the most difficult time making the adjustment to a new high school. Schiller concluded that urban students, particularly those who are minority and have limited resources, need better programs to assist them in making the transition from middle school to high

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school. Such programs not only help students adjust socially and psychologically to their new school environment but also clearly articulate the consequences of taking specific courses in terms of postsecondary school admission. High School Course Selection During the 1980s a major criticism of the American high school cited by the National Commission on Excellence in Education (1983), which called attention to many school practices in dire need of reform, was that high schools offered too many introductory nonacademic courses. Powell, Farrar, and Cohen (1985) found that many students could shop around for their “course of choice.” Since the 1983 publication of A Nation at Risk, there has been a concerted effort by many states to raise academic course requirements in high school. Mathematics has been one of the subjects most affected by these increased requirements (Medrich, Brown, and Henke 1992). In looking at total units of course work, there did not appear to be a significant difference among units of English, science, social studies, and mathematics taken by all high school students (Blank and Gruebel 1993). Examining the data by racial and ethnic groups, however, researchers found that African American high school graduates were still less likely than white graduates to have taken advanced science and mathematics courses (U.S. Department of Education, 1995). Course selection is particularly important since it determines postsecondary school admission and the likelihood of selecting science and mathematics in college (Hoffer 1995; Smith 1995a). NELS:88 data from the second follow-up study show that black high school graduates were twice as likely as white graduates to have taken remedial mathematics and were less likely to have taken higher level mathematics courses such as intermediate algebra, geometry, and trigonometry (U.S. Department of Education 1994a). Selecting geometry appears to be directly related to college admission. More than 80 percent of black students, 82 percent of Hispanic students, and 83 percent of white students who took geometry attended college within four years of high school graduation. The gap in college admission between black and Hispanic minorities, on the one hand, and other groups virtually disappears among students who took geometry (U.S. Department of Education 1994a). Completing a year of high school geometry was found to be related not only to college admission but also to whether a student completes college. Pelavin and Kane (1990) reported that only one in twenty white students, one out of every forty black students, and one in sixty Hispanic students who had less than a year of geometry attained a bachelor’s degree or college senior status within four years of high school graduation. The differences between science course selection among racial and ethnic groups do not appear as pronounced as the differences in mathematics course selection. African Americans were as likely as whites to have taken biology. But at the higher levels, one again finds differences among racial and ethnic groups. African Americans were less likely than whites to have taken chemistry, physics, or a combination of biology, chemistry, and physics (U.S. Department of Education, 1994a).

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With respect to advanced placement (AP) course work in mathematics and science, the highest participation rates were found to be among Asians, 18 percent of whom took calculus and 17 percent took biology. Only 4 percent of black and Hispanic students took AP exams in calculus and biology (Blank and Gruebel 1993). Moreover, black college-bound graduates were far less likely than white graduates to have taken at least two years of a foreign language in high school, which could affect their chances of attending a selective college (U.S. Department of Education 1995). One might suspect that these differences in course selection behavior were related to differences in student achievement. In fact, data from three national studies—the National Assessment of Educational Progress (NAEP), NELS:88, and HS & B—show that test-score performance differences among racial and ethnic groups have narrowed over time. Even though the gap appears to be closing, minorities are still overrepresented at the bottom and underrepresented at the top. Academic Performance in Mathematics and Science NAEP, a national test given to cross-sectional cohorts of students, shows that while there have been some improvements in overall mathematics performance, learning remains highly differentiated across racial and ethnic groups (U.S. Department of Education, 1994a). In 1990, most black and Hispanic students lacked proficiency in higher level mathematics and science (Suter 1993). Less than 0.5 percent of black students and 1 percent of Hispanic students in the twelfth grade demonstrated an understanding of advanced mathematics applications. Recent analyses by Phillips (1996) show that, on average, African Americans score about four-fifths of a standard deviation below whites on the NAEP mathematics tests. (These differences in the standard deviation between African Americans and whites are fairly consistent across age groups for which NAEP is administered: nine-year-olds, thirteen-year-olds, seventeen-year-olds.) Hispanics tend to score about two-thirds of a standard deviation below whites. Disparities in science scores on the NAEP test across age levels among racial and ethnic groups were not as great as those in mathematics. Just 3 percent of Hispanic students and 1 percent of black students in twelfth grade showed an understanding of advanced science applications. Hispanic students performed better on science tests than did black students in the fourth, eighth, and twelfth grades, but they did not perform as well as Asians or whites (Suter 1993). Similar findings regarding differences in test-score performance among racial and ethnic groups were also found in analyses of NELS:88. Data revealed that in eighth grade 28 percent of Hispanics, 30 percent of blacks, and 32 percent of American Indians, as compared with 16 percent of whites and 14 percent of Asians, did not demonstrate mastery of simple mathematics operations (U.S. Department of Education 1990). Such skills are generally considered necessary to perform everyday tasks. Asian students were clearly overrepresented in the top group (35%), as compared with whites (23%), Hispanics (9%), blacks (6%), and American Indians (6%). In the tenth grade these test-score differences among racial and ethnic groups persist (U.S. Department of Education 1994b). White (27%) and Asian (31%) stu-

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dents were more likely than American Indian (4%), black (6%), and Hispanic (12%) students to achieve the highest level of proficiency in problem-solving mathematics skills. Students in families with low levels of resources were four times more likely to lack basic skills in mathematics than students who had the maximum family resources. Another indicator that tells a similar story is performance on the Scholastic Aptitude Test (SAT). In 1976 blacks scored 139 points lower than whites on the mathematics component of the test (U.S. Department of Education 1995). Since 1976 black performance has improved, but on average blacks still score 106 points lower than whites. Asians, on the other hand, continue to score higher than whites on the mathematics component of the SAT but below whites on the verbal test (Phillips 1996). Test-score differences cannot simply be dismissed as evidence of culturally biased tests. Phillips (1996) reports that most evidence shows that achievement tests predict grades and occupational choices equally well for whites and African Americans. Moreover, differences in cognitive skills have also been shown to be related to the majority and minority gap in employment and earnings. Educational Expectations Despite these test-score differences, the educational expectations of blacks and whites are similar. Non-Hispanic white and black students in the tenth grade do not differ in their perceptions of how far in school they plan to progress. Some 61 percent of whites and 59 percent of blacks expect to complete at least a four-year college degree. Tenth-grade Hispanic students expect to graduate from college or attend graduate school less often (46%) than Asians (69%), blacks (59%) or whites (61%). Hispanic, black, and white high school sophomores similarly report that their mothers hold high postsecondary aspirations for them, although at somewhat lower rates (this difference is statistically significant) than those reported by Asian sophomores (U.S. Department of Education 1994b). Female educational expectations have risen considerably. This is quite pronounced at the graduate level. While both males and females expect to attend graduate school, female expectations have increased even more than male expectations over the past ten years. Female high school sophomores in 1990 were much more likely to expect to achieve a postgraduate degree than those in 1980 (Rasinski et al. 1993). Recent evidence shows that educational expectations are partially shaped and influenced by the high schools (Schneider, Knauth, and Makris 2000). In the 1970s Rosenbaum (1976) found that in some schools African Americans are more likely to be advised to enter community colleges, the armed services, or the labor force immediately after high school graduation. In other schools there were elaborate programs designed to introduce students to college life and help them with the application process and securing financial aid. Today the sorting process of moving minorities into noncollege programs appears much less prevalent than in the 1970s. The message in high schools is that everyone is going to college. However, the extent of information regarding the college application process given to minority groups varies from school to school (Schneider, Knauth, and Makris 2000). In some cases high schools have instituted

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programs to guide African Americans and Hispanics into science and engineering programs by offering them substantial financial incentives. Why certain schools advise students into one career path rather than another seems to be linked to community values and local community resources, such as the presence of a wide selection of colleges and universities, rather than being related to the individual interests and biases of the high school counselor. The importance of the high school guidance counselor appears to be more site-specific than person-specific, as was found by Rosenbaum (1976) and Cicourel and Kitsuse (1963). Occupational Expectations Even as early as the eighth grade, students have some idea about the type of job they would like to have at age thirty (Schneider 1994). However, a large number of students change their minds about these occupational expectations between the eighth and tenth grades. Schneider, using data from NELS:88 base year and the first follow-up study, found that students with higher self-esteem and feelings of control over their lives are less likely to change their earlier career choices. Students who do not change their career choices may be clear about the occupation they desire and may have begun to make conscious decisions about their future lives. Whether these decisions are fixed, rational, informed, or realistic at the eighth-grade level is open to question. Another factor related to occupational change is performance in mathematics (Schneider 1994). Students who do not change their career plans from the eighth to the tenth grade are more likely to have higher mathematics test scores than students who change their plans. A similar trend appears to hold for science grades, but it is not as pronounced. Students who perform best in school and feel most assured and in control are least likely to change their career decisions from the eighth to the tenth grade. This suggests that decisions about career choices are influenced by conceptions of self as well as by external factors. The Alfred P. Sloan Study on Youth and Social Development (Czikszentmihalyi and Schneider 2000) examines beliefs, attitudes, and values adolescents have regarding their future schooling and work. This five-year longitudinal study is one of the few to include a national representative of U.S. youth stratified by race, ethnicity, and social class. Over twelve hundred students in the sixth, eighth, tenth, and twelfth grades in 1992 were administered a series of tests to measure their attitudes toward education, their knowledge of adult work, and their career aspirations. First-year results from this study reveal that most adolescents’ expectations of what jobs they will have as adults are extraordinarily high, given the proportionate number of expected job openings in the future. For example, 10 percent of the students expect to become physicians and about one out of three expects to have a professional career. Another 10 percent expect to have careers in athletics or the entertainment industry. Even in the last year of high school, students’ expectations for a professional or athletic career are about five hundred times greater than projected employment possibilities. Few twelfth graders, irrespective of racial, ethnic, or socioeconomic class, imagine themselves as adults working in blue-collar, service, or craft occupations.

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These occupational goals are highest in the sixth grade and decrease somewhat as students move through high school. Gender stereotyping in the early grades is also quite strong, especially among youngsters with limited family and community resources. Girls are more likely to envision themselves as teachers, social workers, and nurses, whereas boys are more likely to see themselves as engineers and policemen. The unrealistic occupational choices of young people across all racial and ethnic groups points to the importance of finding ways for young people to become more informed about the workplace. Young people need to have access to additional information about the wide range of skills and knowledge current and prospective jobs require. This is especially true for adolescents who are less likely to have family or community role models or other sources of information by means of which they can obtain a realistic understanding of the educational credentials and social skills necessary for various types of jobs.

Educational Attainment: High School Graduation and College Admission The convergence of educational expectations and occupational aspirations along racial, ethnic, and gender lines has not translated into comparable levels of educational attainment, especially among African Americans and Hispanics (Kao, Tienda, and Schneider 1996). Young adults may become increasingly pessimistic about their plans for postsecondary school when faced with entrance requirements and financial expenses related to postsecondary school attendance, particularly in competitive four-year institutions (Kao and Tienda 1995). Lack of information, especially for minorities with lower levels of parental education, can be a deterrent not only in terms of seeking admission but also in applying oneself in school. Using census data, Mare (1995) estimated the probabilities of students continuing through successive levels of schooling. He found that from 1980 to 1990 all major racial and ethnic groups experienced increased average rates of schooling and levels of educational attainment. But racial and ethnic differences in school continuation emerged during high school and grew larger during the transition to college. Asians continued to have the highest probabilities of completing each successive year of schooling, followed by whites. Blacks and Hispanics experienced similar progression probabilities, albeit somewhat lower than those of whites. American Indians had the lowest continuation probabilities at almost all transitional points. From 1980 to 1990 these differentials became somewhat smaller at the secondary level and in the transition period between high school and college, but they persisted among those persons who reached college. Looking more closely at educational attainment, one finds that all groups have dramatically increased their high school graduation rates since 1940. In fact, black, American Indian, and Hispanic students have made the greatest gains—especially since 1960—and have therefore converged toward the rates of white and Asian students (U.S. Department of Education 1995). When examining postsecondary school admissions, it appears that blacks are less likely than whites to make an immediate transition from high school to college. In 1991 at the bachelor’s degree level, Afri-

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can Americans were more likely than whites to major in business and management or in the computer and information sciences, but they were less likely to major in engineering, the humanities, education, and the health sciences (U.S. Department of Education 1995). The probabilities of making the transition into college and completing a bachelor’s degree increased for Asians and whites from 1980 to 1990 and remained relatively unchanged for other racial and ethnic groups. Mare (1995) concluded that inequality of educational attainment progresses from earlier to later stages of the schooling process. As schooling becomes nearly universal at the elementary and secondary levels, inequalities are eliminated, but inequalities persist or even increase at the postsecondary level. In his analysis Hauser (1988) showed that chances for college admission for black students peaked in 1977, declined through the early 1980s, and did not recover. During this same period, the chances for college admission for white students grew at an unprecedented rate. Hauser maintained that the decline in college entry for blacks could not be explained by changes in family income, academic achievement, decreased interest in four-year programs, or increased interest in technical, vocational, or two-year colleges. Rather, he suggested that what is driving down college attendance among blacks is that college has become increasingly unattractive in terms of financial and social support. Not only is the college financial aid package smaller relative to need, but it is also increasingly targeted to meet the needs and preferences of middle-income families. Majoring in Science and Mathematics in College African Americans, Hispanics, and Native Americans are underrepresented among those earning higher education degrees in science and engineering (Suter 1993). One possible explanation for this underrepresentation is higher than average rates of attrition in these fields. HS & B data show that just 21 percent of African American, Hispanic, and Native American freshmen in 1980 who intended to major in the natural sciences or engineering actually graduated with a degree in one of these fields by 1986, compared to 43 percent of students of other races and ethnicities (National Science Foundation 1990). Factors associated with these trends are inadequate precollege academic preparation, historical barriers blocking entry into certain fouryear institutions, and other economic conditions (National Science Foundation 1989; Office of Technology Assessment 1992).

Thinking About the Future: Some Concluding Remarks In preparing students for an uncertain future, we need to obtain a clearer understanding about how young people form ideas about work and what influences their decisions. The research models used to pursue these questions have generated important insights, but they are not sufficient for a proper understanding of this increasingly complicated process. Too little attention has been paid to the contexts that affect occupational choice among different racial and ethnic groups. Equally important

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are the actual interactions that students have with their families, friends, and teachers. For example, we need to look more carefully at how such family characteristics as educational level and expectations and such school activities as transition programs and course selection affect admission to postsecondary schools. By examining these interactions over time, it will be possible to isolate those factors that motivate different students to choose science, mathematics, and engineering. Most empirical studies do not view occupational choice as an evolving process influenced by external factors that can change over time. To understand the factors affecting career choice, we need to recognize the importance of this evolving process, which is continually being influenced by both individual and situational conditions. The influences of family, peer group, and school community on postsecondary school and career choices constantly interact with the formation of the self. This dynamic process produces an ongoing realignment of decisions that are shaped by the individual’s emerging sense of efficacy and the social contexts within which he or she functions. These factors variously influence the process, depending on the individual’s personal development and the social resources that he or she has accumulated. Interest in science in the eighth grade will have a limited impact on a student unless this interest is nurtured in the family and through various policies formulated by schools. What happens to a student in eighth grade, as a freshman, and throughout high school will lay the foundation for future postsecondary career choices in science. Many decisions about future careers are colored by the world outside the home. Labor market opportunities in the community also shape a student’s preferences for certain types of work. Gender, race and ethnicity, and cognitive abilities also influence how a student thinks about work. These individual ascribed characteristics interact with the social contexts of the family, peer group, school, and community to shape occupational expectations. Wanting to be an engineer, studying to be an engineer, and graduating from college and finding a job as an engineer are not a result of luck but of social opportunities, and not everyone has access to the same social opportunities and support. The most important research we can undertake is to examine more closely the social opportunities and constraints ethnic and racial minorities face throughout elementary, secondary, and postsecondary schooling that impede or encourage successful career choices in science, mathematics, and engineering.

References Alexander, K. L., and M. A. Cook. 1982. Curricula and coursework: A surprise ending to a familiar story. American Sociological Review 49: 748–802. Blank, R. K., and D. Gruebel. 1993. State Indicators of Science and Mathematics Education 1993. Washington, DC: Council of Chief State School Officers. Carnegie Council on Adolescent Development. 1990. Turning Points: Preparing American Youth for the 21st Century. New York: Carnegie Corporation of New York. Cicourel, A. V., and J. I. Kitsuse. 1963. The Educational Decision-Makers. Indianapolis, IN: Bobbs-Merrill. Coleman, J. S. 1987. Families and Schools. Educational Researcher 16: 32–38. Coleman, J. S., E. Q. Campbell, et al. 1966. Equality of Educational Opportunity. Washington, DC: U.S. Government Printing Office.

Explaining The Transition the Unrealized to and from Aspirations High School of Racial of Ethnic and Minority Ethnic Minorities Students 185 Csikszentmihalyi, M., L. Hedges, and B. Schneider, eds. 2000. Images and Experiences of Work in American Adolescents. New York: Basic Books. Duncan, G. J., J. Brooks-Gunn, and P. K. Klebanov. 1994. Economic deprivation and early childhood development. Child Development 65: 296–318. Eccles (Parsons), J. 1983. Expectancies, values, and academic behaviors. In J. T. Spence, ed., Achievement and Achievement Motives, 75–146. San Francisco: W. H. Freeman. Entwisle, D., and K. Alexander. 1993. Entry into schools: The beginning school transition and educational stratification in the United States. In Annual Review of Sociology (vol. 19), 401–23. Palo Alto, CA: Annual Reviews, Inc. Gamoran, A., and M. Berends. 1987. The effects of stratification in secondary schools: Synthesis of survey and ethnographic research. Review of Educational Research 57: 415– 436. Gamoran, A., and R. D. Mare. 1989. Secondary school tracking and educational inequality: Compensation, reinforcement, or neutrality? American Journal of Sociology 94: 1146–83. Green, P., B. Dugoni, S. Inyels, and E. Camburn. 1995. A Profile of the American High School Senior in 1992. Washington, DC: U.S. Department of Education. Hallinan, M. 1994. Tracking: From theory to practice. Sociology of Education 67:79–84. Hallinan, M., and R. A. Williams. 1989. Interracial friendship choices in secondary schools. American Sociological Review 54: 67–78. Hauser, R. M. 1988. Declining black college entry: How did it happen? Working Paper 88– 11. Madison, WI: Center for Demography and Ecology, University of WisconsinMadison. Hoffer, T. 1995. High school curriculum differentiation and postsecondary outcomes. In P. W. Cookson and B. Schneider, eds., Transforming Schools, 371–402. New York: Garland. Hotchkiss, L., and L. Dorsten. 1987. Curriculum effects on early post–high school outcomes. In R. G. Corwin, ed., Sociology of Education and Socialization, 191–219. Greenwich, CT: JAI Press. Huston, A. C., V. C. McLoyd, and C. Garcia Coll. 1994. Children and poverty: Issues in contemporary research. Child Development 65(2): 275–282. Kao, G., and M. Tienda. 1995. Optimism and achievement: The educational performance of immigrant youth. Social Science Quarterly 76: 1–18. Kao, G., M. Tienda, and B. Schneider. 1996. Racial and ethnic variation in the educational outcomes. In A. M. Pallas, ed., Research in Sociology of Education and Socialization. Greenwich, CT: JAI Press. Knauth, S., B. Schneider, and E. Makris. 1995. The influence of guidance counselors: School patterns. Paper presented at the annual meeting of the American Educational Research Association, San Francisco. Lee, V. E., and A. S. Bryk. 1988. Curriculum tracking as mediating the social distribution of high school achievement. Sociology of Education 62: 78–94. Levin, H. M., ed. 1993. Review of Educational Research (special issue) 63(3): 245–376. Mare, R. D. 1995. Changes in educational attainment and school enrollment. In R. Farley, ed., State of the Union: America in the 1990s. Vol. 1: Economic Trends. New York: Russell Sage Foundation. McKnight, C. C., F. J. Crosswhite, J. A. Dossey, E. Kifer, J. O. Swafford, K. J. Travers, and T. J. Cooney, 1987. The Underachieving Curriculum: Assessing U.S. School Mathematics from an International Perspective. Champaign, IL: Stipes Publishing. Medrich, E. A., C. L. Brown, and R. R. Henke. 1992. Overview and Inventory of State Requirements for School Coursework and Attendance. (NCES 92–663). Washington, DC: National Center for Education Statistics.

186 Adolescence Onward Murray, C., and R. Hernstein. 1992. What’s really behind the SAT-score decline? Public Interest 106: 32–56. National Commission on Excellence in Education. 1983. A Nation at Risk: The Imperative for Educational Reform. Washington, DC: The National Commission on Excellence in Education. National Science Foundation, Division of Policy Research and Analysis. 1990. The State of Academic Science and Engineering. Washington, DC: National Science Foundation (NSF 90-35). National Science Foundation, Division of Science Resources Studies (NSF-SRS). 1992. Science and Engineering Degrees: 1996–1990. Washington, DC: National Science Foundation (NSF 92-326). Natriello, G., A. M. Pallas, and K. Alexander. 1989. On the right track? Curriculum and academic achievement. Sociology of Education 62: 109–118. Oakes, J. 1985. Keeping Track: How Schools Structure Inequality. New Haven, CT: Yale University Press. ——— . 1990. Multiplying Inequalities: The Effects of Race, Social Class, and Tracking on Opportunities to Learn Mathematics and Science. Santa Monica, CA: Rand Corporation. ——— . 1994. More than misapplied technology: A normative and political response to Hallinan on tracking. Sociology of Education 67: 84–89. Oakes, J., A. Gamoran, and R. N. Page. 1992. Curriculum differentiation: Opportunities, outcomes, and meanings. In P. W. Jackson, ed., Handbook of Research on Curriculum: A Project of the American Educational Research Association. New York: Macmillan. Office of Technology Assessment (OTA). 1989. Higher Education for Science and Engineering: A Background Paper (OTA–BP–SET–52). Washington, DC: U.S. Government Printing Office. Pelavin, S. H., and M. Kane. 1990. Changing the Odds: Factors Increasing Access to College. New York: College Entrance Examination Board. Phillips, M. 1996. Early inequalities: Ethnic differences in cognitive growth during childhood. Paper presented at the annual meeting of the American Sociological Association, New York. Powell, A. G., E. Farrar, and D. K. Cohen. 1985. The Shopping Mall High School: Winners and Losers in the Educational Marketplace. Boston: Houghton Mifflin. Rasinski, K. A., S. J. Ingels, D. A. Rock, J. M. Pollack, and S. Wu. 1993. America’s High School Sophomores: A Ten-Year Comparison (NCES 93–087). Washington, DC: National Center for Education Statistics. Rosenbaum, J. E. 1976. Making inequality: The Hidden Curriculum of High School Tracking. New York: Wiley. Schiller, K. 1995. Managing the chaos: Organizational Efforts to Reduce Uncertainty in the Transition of Students to High School. Ph.D. diss., University of Chicago. Schneider, B. 1994. Thinking about an occupation. A new developmental and contextual perspective. In A. M. Pallas, ed., Research in Sociology of Education and Socialization, 239–259. Greenwich, CT: JAI Press. Schneider, B., S. Knauth, and E. Makris. 2000. Guiding students into the future: Three schools of thought. In C. Bidwell, M. Csikszentmihalyi, L. Hedges, and B. Schneider, eds. Images and Experiences of Work in American Adolescents. New York: Basic Books. Slavin, R. E. 1990. Achievement effects of ability grouping in secondary schools: A bestevidence synthesis. Review of Educational Research 60: 471–500. Smith, J. B. 1995a. Does an extra year make any difference? The impact of early access to algebra on long-term gains in mathematics attainment. Typescript.

The Transition to and from High School The of College EthnicPreparation Minority Students Process 187 ———. 1995b. Getting to high school: The lasting effects of eighth-grade transition programs on retention and experiences in high school. Typescript. Stevenson, D. L., K. S. Schiller, and B. Schneider. 1994. Sequences of opportunities for learning. Sociology of Education 67: 184–198. Suter, L. E., ed. 1993. Indicators of Science and Mathematics Education 1992. Washington, DC: National Science Foundation. U.S. Bureau of the Census. 1992. Poverty in the United States, 1992. Current Population Reports, series P–60, no. 185. Washington, DC: U.S. Government Printing Office. U.S. Department of Education. 1990. A Profile of the American Eighth Grader. (NCES 90– 458). Washington, DC: National Center for Education Statistics. ——— . 1994a. The Condition of Education 1994. (NCES 94–149). Washington, DC: National Center for Education Statistics. ———. 1994b. A Profile of the American High School Sophomore in 1990. (NCES 95–086). Washington, DC: National Center for Education Statistics. ———. 1995. The Educational Progress of Black Students. (NCES 95–765). Washington, DC: National Center for Education Statistics. Useem, E. 1991. Student selection into course sequences in mathematics: The impact of parental involvement and school policies. Journal of Research on Adolescence 1: 231–250. Usiskin, Z. 1987. Why elementary algebra can, should, and must be an eighth-grade course for average students. Mathematics Teacher 80: 428–438. Wilson, W. J. 1987. The Truly Disadvantaged: The Inner City, the Underclass, and Public Policy. Chicago: University of Chicago Press.

judith shay

The College Preparation Process I represent Cooperating Hampton Roads Organizations for Minorities in Engineering (CHROME), a precollege program in Tidewater, Virginia, as well as members of the National Association of Precollege Directors (NAPD), who have similar programs across the country. We are among the practitioners in the field. Many of us are offshoots of NACME’s precollege effort—precious seeds planted over the years, some with roots and branches that are interrelated like many of our family trees. For example, CHROME evolved from the efforts of Southeastern Consortium for Minorities in Engineering (SECME) in Virginia. We primarily serve precollege students. It is important to note that many of us in the field have perceived the need to expand our programs downward into the ele-

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mentary level. NAPD members are geographically distributed throughout the country and have at least three things in common: (1) we implement comprehensive yearround programs; (2) we maintain extensive longitudinal databases on our students; and (3) we have full-time directors. We have been described by Clewell, Anderson, and Thorpe, among others, as “intervention programs.” This is certainly true. We intervene for our students and provide experiences external to their instruction, including career information; role models; work site visits, where students can see what scientists and engineers do; Saturday Academies on college campuses using research experiences and group activities, where students also learn about the college preparation process; summer camps; and internships. We do whatever our needs assessment and internal monitoring indicate is needed. For example, through our data collection, we at CHROME identified a need to attract more African American men to our program. Their participation was falling way behind their female counterparts (roughly 27 percent of our participants were African American men, while over 46 percent were African American women.) Consequently we developed a Summer Academy in Mathematics and Science for young African American men. One of the benefits of NAPD is we share our program successes. PRIME in Philadelphia identified a similar need, and may develop a similar program next year. We also had a summer program for women, both minority and white. Some say that you theoretically cannot serve both groups because they have different sets of obstacles to overcome; however, we have found commonality in their needs. Both groups need exposure to career possibilities, role models who look like them, successful experiences in mathematics and science, and help with goalsetting, exploring the future, and controlling their environment. The programming was, of course, somewhat different as well. For men we explored the African American culture and worked on conflict resolution; for women we cultivated networking skills and explored how to develop group support systems. Many of the NAPD programs not only intervene but also aim to change the environment in which our students are educated. We work with teachers and counselors. This includes both SECME Institutes and others designed to develop teams of teachers to work with underrepresented students in their schools, and workshops developed for faculty, counselors, and administrators within the schools who are not directly working with our program. These sessions might include multiculturalism, learning styles, cooperative learning, and tying the real world to the classroom. Many of us are involved in NSF’s Statewide Systemic, Rural, and Urban initiatives. We also sponsor and support parenting programs and involve the entire community. In addition to working with these other aspects of the educational system, the system can be influenced by the development of a critical mass of minority students who are achieving and succeeding in mathematics in science. Our program has over 2,400 students actively involved this year. This visible group of successful and highachieving students can change the perceptions of their peers as well as their teachers and counselors. And when a program is administered comprehensively within a community or region, it can begin to influence public perception as well. Consequently, I believe our programs are more systemic than they are credited for.

The Transition to and from High School The of College EthnicPreparation Minority Students Process 189

For grades 8 through the first year of college, the research seems to indicate that course selection is a critical variable in the process of preparing for college study in SEM. Most practitioners and many researchers have identified the middle school years as the time when key choices are made. All sorts of variables, including perceptions based on race, gender, and family structure, often lead to students not being placed in the pre-algebra or algebra courses in middle school. Another critical period is the transition between tenth and eleventh grade, where in many systems mathematics and science become optional or are split between “academic” and “vocational” or “tech prep.” Yet where is the research on the importance of guidance counselors’ perceptions (or bias) in this process? As a practitioner, I see this as a gatekeeper to many students’ future. Parents and students must be educated on the course-selection process so that when they are misdirected by school counselors, they will insist on correction. In their discussion of the influence of peers, Ginorio and Grignon state that innercity African American youth tend to value peer-related competencies more than school-related ones, and that the peer group culture supports negative attitudes toward school. But what research has measured the impact of a positive peer support group within each school for these students? This is one of the CHROME program’s strengths. When minority high school students participate in a CHROME club, and are invited to a special program to meet the Secretary of Energy or join students from twenty other high schools in a regional leadership academy or attend a college fair developed just for CHROME students, these experiences increase the likelihood that they will continue on the path to successful preparation for SEM study in college. As has been said, “Students acquire a sense of significance from doing significant things.” Ginorio and Grignon also refer to the persistence factor. They point out that many African American students may be able to persist even in the face of discrimination. What research is available on the persistence factor? I hypothesize that the younger students are when they decide to pursue SEM, and the more they are nurtured and supported along the way, the better prepared they will be and, more important perhaps, the more likely they will be to persist when things get tough. The University of Virginia and Virginia Tech have said that they recruit CHROME students because they are better prepared than most of their minority students, and are more likely to be retained because of their precollege experience and exposure to and knowledge of their field of study. The persistence factor and linkages from precollege to college are very important. We have worked with universities to develop informal support groups through their CHROME alumni. These students come from different high schools but have an immediate connection with other CHROME students in their class. They also develop big brother/sister associations that support them throughout their college experience. Universities should form closer links to their precollege feeder programs and use that experience to help students through their first year of study. The NAPD programs would certainly lend themselves to further study. Each has large and varied student populations representing many socioeconomic groups and geographic settings, and each program has an existing database and close rela-

190 Adolescence Onward

tionship with participating schools and school districts. As Clewell and Braddock point out, “Research has reached the stage where more in-depth and disaggregated probing is called for to get at the causes as well as the solutions for underparticipation and representation of students of color in mathematics and science.” The member programs of NAPD would lend themselves to this effort and be willing participants in a national agenda to increase the number of women and minority students successfully preparing for careers in science, mathematics, and engineering.

r. guy vickers

Policy Matters I’d like to illustrate the policy issues at stake through an analogy that often runs through my mind. As a mature ex-jock, I am amazed that we can identify athletes in grades four and five and track them for success. I’m not knocking athletics or organized sports; I’m from that community. But I look at what happens across college campuses when we bring in blue-chip athletes. We provide the best services and facilities for them; nurture, encourage, and pamper them; and do everything necessary to help them achieve success. Then I look at what we do with the bright students we attract to our college campuses. We make them jump through hoops, over hurdles, and tell them that one out of three will fail. We really need to take a hard look at what we are doing to these students, for it is a major factor in driving kids away, particularly from the math and science majors. One thing that we do at our SECME competition is directly related to sports. In the past, when our winners came from middle grade and high school teams, we would give them small-size trophies and medallions as awards. One day it dawned on me that when I was in school and played sports, I received huge trophies, and that inspired me a little bit. So now we give oversized trophies to middle grade kids who can’t even pick them up. You should see the smiles on these kids’ faces when they try to pick up that trophy. We also give them cash prizes that industry has contributed. A couple of years ago, the winners of the mousetrap car competition held at North Carolina A&T were at Atlanta’s airport. They all had their SECME T-shirts on, and each kid had received a trophy. People kept stopping them, wanting to know what sports team they were on. They answered with obvious pride, “We are math and science competitors.” That’s the kind of spunk we see in the kids. We know they can perform and produce if we give them a chance. I would love to see what the research says on how we treat athletes versus how we treat students.

v

THE UNDERGRADUATE YEARS PLUS ONE

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reginald wilson

Barriers to Minority Success in College Science, Mathematics, and Engineering Programs his chapter will explore the continued underrepresentation of minorities in sci-

T ence, mathematics, and engineering. Since such exploration must begin with

precollege preparation, I intend to address the subject first. But the thrust of my efforts will focus on the major impact of the undergraduate years and their various influences: retention and support programs; the campus climate; the effect of minority faculty; curricular changes and their impact on student learning; and what research can tell us to improve retention and increase access.

Elementary and Secondary Education The early years of schooling, of course, have an enormous impact on later success in college, both in terms of access to competitive programs and the ability to complete a degree. Minorities, while still lagging behind whites, have made great strides in high school completion. As can be seen in table 1, African Americans have substantially narrowed the gap with whites; indeed, African American females are almost equal to white males in high school completion. Hispanics, while still suffering an inordinately high dropout rate, have gained nearly six points in high school completion (Carter and Wilson 1993). One should note, however, that high school completion reflects both graduation from secondary school as well as completion through GED and returning to adult education after dropping out. Therefore, the enormous dropout rates recorded in inner-city schools are real, but minorities are increasingly completing high school later as they become aware of the necessity of further education in order to be more competitive in the labor market. High school completion through equivalency, while representing an achievement, consists of attaining a satisfactory score 193

194 The Undergraduate Years plus One table 1 High School Completion Rates (%)

White African American Hispanic

1972

1992

Gain

81.7 66.7 51.9

83.3 74.6 57.3

+1.6 +7.9 +5.4

Source: Bureau of the Census 1994.

in liberal arts subjects without exposure to science laboratory courses and technical subjects, thus limiting such students’ preparation for majoring in science, mathematics, and engineering upon entering college. The gap between graduating from high school and attending college has remained wide over the years, especially for males, and has actually shown a regression for African American males (see table 2). This can partially be explained by African American overrepresentation in the armed forces and in proprietary schools. African American males account for nearly 27 percent of the army’s soldiers and are overrepresented in every other branch of military service (Wilson and Carter 1988). Moreover, African American recruits are more likely to be high school graduates than white recruits (Hexter 1988). However, among other factors affecting African American male college attendance are an inordinately high unemployment and poverty rate. Minority enrollment in proprietary schools stands at nearly 35 percent. Minority women (as well as all women) made great strides in the past two decades in the area of college attendance, surpassing men of every ethnic group except Asian males. However, they are still underrepresented in science and engineering. For minority men and women, the reality of their experience in inner-city schools can have an enormous impact on choice of college and eventual major. Schools in central cities “consigned [African Americans and Hispanics] to inferior schooling under an institutionalized system” (Farley and Allen 1987:189) that still prevails today.

table 2 Enrolled-in-College Rate (%) 1972

1992

White men women

38.6 26.6

41.6 42.8

African American men women

33.0 22.5

29.7 37.5

Hispanic men women

30.6 22.3

34.3 39.4

Source: Bureau of the Census 1993.

Barriers to Minority Success

195

Schools attended by African Americans are more likely to be segregated, with inferior physical facilities and laboratories. Schools attended by Hispanics are even more segregated than African American schools and are plagued by the same differentials in facilities and equipment. Even middle-class African Americans living in suburbs and attending predominantly white schools are likely to suffer discrimination and racism (Feagin and Sikes 1994). Moreover, African Americans (especially males) “are also disciplined, expelled, and suspended at higher rates than any other group because their styles of behavior are seen as disruptive by many teachers. One national study . . . found that African Americans account for 29 percent of suspensions, 27 percent of expulsions, and 29 percent of corporal punishments” (Farley and Allen 1987). Among Hispanics, “almost half (49.1 percent) had not completed high school, compared to one-fifth, or 21.2 percent, of non-Hispanics. . . . Hispanic high school graduates have lower levels of college participation than the total population” (Carter and Wilson 1990). Thus, the discrepancy in school achievement for minorities is the result of several factors: “(1) racial and ethnic segregation in schools; (2) language and cultural biases in school practices; (3) limited academic achievement of students; (4) dropping out of school; (5) limited school financing; (6) poor—or low—quality teacher– student interaction; (7) tracking and curriculum differentiation” (Justiz, Wilson, and Bjork 1994:160), to which should be added lack of minorities in the teaching force and less choices of advanced mathematics and science courses due to the lack of laboratories and trained teachers. As can be seen in tables 3 and 4, minorities are much less likely than whites or Asians to pick an academic course of study or to take advanced mathematics and science courses in high school. Therefore, they are less prepared for a rigorous college academic program or a selective university. As difficult as these barriers are to overcome, some elementary and secondary schools have managed to surmount them with great effort. A school possessing the profile of Madison School (see table 5) is doomed to failure. Yet this school has the highest scores in reading and mathematics of any school in Pittsburgh. This attainment was due to the enlightened leadership of the principal and a dedicated staff of teachers who would not let the predictive validity of normal school demographics deter them. This meant that they: (1) placed great emphasis on maintaining discipline; (2) gave more time to academic subjects by (3) eliminat-

table 3 High School Program, by Race (%)

Asian White Hispanic African American American Indian

Academic

Career

General

Other

73.4 79.9 65.2 64.0 66.7

4.7 6.2 16.8 15.3 10.1

20.9 13.5 16.8 19.5 22.4

1.0 .4 1.2 1.2 .8

Source: Ramist and Arbeiter, College Bound Seniors, 1990.

196 The Undergraduate Years plus One table 4 Mean Number of Years in Selected Courses

White Asian African American Hispanic American Indian

Math

Foreign language

Science

3.66 3.81 3.38 3.34 3.39

2.27 2.38 1.99 2.36 1.82

2.87 2.05 1.50 1.04 1.69

Source: Ramist and Arbeiter, College Bound Seniors, 1990.

ing or reducing recess, music, art, and gym; (4) had high expectations; and (5) made a contractual agreement with parents to monitor homework. Exemplary elementary and secondary schools like Madison School do exist but are unfortunately few in number. It not only takes hard work but—even more difficult to attain—a belief by educators that the human mind is infinitely malleable and that standardized test predictions can be changed and student achievement raised to a level of excellence.

Institutional Climate of Undergraduate College: Access As was noted in the previous section, minorities approach college with serious academic handicaps that severely limit their access to competitive universities and rigorous academic programs. Moreover, schools that emphasize SAT scores to determine admission may also be a limiting factor to minority access, this despite the fact that minorities have made the largest gains in SAT performance during the past twenty years (see table 6). An exception must be noted for the Asian minority, which, when considered collectively, appears to represent the highest achievement both in SAT scores and in competitive academic programs; when disaggregated, however, Asians reflect both the highest and lowest levels of academic achievement (Suzuki 1994). For example, the Hmong, Cambodians, and Pacific Islanders have some of the lowest academic achievement and high school graduation rates, while the Chinese and Japanese have some of the highest. In terms of college admissions, the Chinese and Japanese are above the national norm, while Southeast Asians have increasingly displayed violent and antisocial behavior on the West Coast (Suzuki 1994).

table 5 Profile of Madison School, Pittsburgh Percentage of students Below poverty With single parent African American Above norm for reading Above norm for math Source: Adapted from Sizemore 1988.

70.0 60.0 98.5 84.0 78.0

Barriers to Minority Success

197

table 6 SAT Scores for Selected Years

White Asian African American Hispanic American Indian

1976

1994

Gain

944 932 686 781 808

938 951 740 799 837

—6 +19 +54 +18 +29

Source: Educational Testing Service 1995.

On the other hand, while evidencing less advanced verbal language skills, Asians are disproportionately overrepresented in the sciences and engineering and vastly underrepresented in the humanities and the liberal arts (Suzuki 1994). Furthermore, most Asians live in the suburbs and, as a result, generally attend better elementary schools and have access to more competitive high schools. Asian parents also place a heavier emphasis on education. Although these factors may partially explain Asian success in the sciences and in engineering, much more research needs to be done to determine the reasons for Asian deviation from the norm of other minorities (Suzuki 1994). Most minorities face college admissions policies with some severe disadvantages, not the least of which are institutional practices, particularly in the sciences and engineering, that assume that all students are immediately ready for a rigorous and heavy course of study without too much help; that is the “corporate climate” of most math-based disciplines of study (Fleming, Brown and Watkins 1992). Thus, high entrance requirements (as well as cost) means that for most minority students the point of entry to higher education will be the open-door community college. This can have both good and bad consequences. As a primarily teaching institution, the community college faculty can concentrate on overcoming students’ deficiencies in various skills and giving them a solid foundation in the basics. On the other hand, students beginning their education in a community college have great difficulty in transferring to a baccalaureate institution and completing a B.A. degree (Rendon and Nora 1994). Indeed, some would claim that attendance at a community college is an impediment to attaining a B.A. degree (Paul 1993). As can be seen in table 7, minorities are heavily represented in community colleges and their transfer rates leave much to be desired. Although Asians go to community colleges because of the lower cost, they do so with the intention of transferring. Other minorities go to community colleges because of admission restrictions at four-year schools, with no clear-cut intention of transferring. This accounts for the differential transfer rates and makes imperative a sharpening of the mission of community colleges to place greater emphasis on transfer and preparation for transfer. Research indicates that community colleges must devote greater attention to: (1) faculty development in order to improve the curricula and strengthen teaching; (2) articulation committees in order to make the transition from two- to four-year schools consistent and smooth; (3) student assessment in order to document that students are learning what they are supposed to; and (4) program evaluation in order

198 The Undergraduate Years plus One table 7 Community College Enrollment and Transfer Rate (%)

White African American Hispanic Asian American Indian

Enrollment

Transfer rate

37 47 56 40 65

25 15 15 40 10

Source: American Council on Education 1993.

to assess the effectiveness of special projects such as transfer centers, remedial studies programs, and so on (Rendon and Nora 1994). The responsibility for facilitating the transfer process rests most heavily on the receiving four-year institution since, as studies indicate, impediments, barriers, and elitism are more generally part of the culture of the university. The university can overcome this by: (1) providing mentors for incoming transfer students; (2) publishing a transfer catalog for the community college explaining the steps in the transfer process; (3) accepting community college credits on previously agreed upon articulation agreements; (4) providing visits to the four-year college campus; and (5) providing financial aid and information on additional grants and loans (Rendon and Nora 1994). As was previously mentioned, the “culture” of the mathematics, science, and engineering programs is one of rigid admission standards, including high SAT scores, competitive and rigorous academic offerings, and an expectation that students will immediately adjust without much help. But research indicates that the retention rate for white students in engineering is only 50 percent and “a disappointing 30 percent for underrepresented minorities” (Fleming, Brown, and Watkins 1992:3). When one notes that engineering students are among the most able students entering college, these dropout rates are alarming and clearly reflect an unacceptable waste of talent. Among the research studies examining why students fail, most focus on student characteristics. Few study faculty or institutional characteristics, which can be as if not more important. Studies have shown that faculty members often have low expectations from minority students, contributing to poor academic performance; and majority students usually mimic the attitudes of faculty, thus “creating an overall climate of isolation” (Morrison and Williams 1993). All of these factors lead to increased attrition rates for underrepresented minority students. Compounding these factors is the serious lack of minority faculty in science and engineering, who would be more sympathetic to nurturing and mentoring minority students.

Dynamics of Keeping Students in Science, Math, and Engineering Programs: Retention As was mentioned previously, minority attrition in science, mathematics, and engineering (SME) programs is primarily due to a hostile, isolationist environment. Future

Barriers to Minority Success

199

study should more properly focus on the institutional environment. It must be emphasized that despite the debilitating effect of inferior secondary schools and programs, the typical minority student majoring in SME is generally the best student who survives despite that background. Thus, he or she is nearly comparable to white and Asian peers in terms of SAT scores and grades. Yet “they drop out of engineering school at double the rate of their classmates” (Campbell 1992a). Among the institutional factors that affect retention of minority students are financial aid in the form of grants, faculty attitudes, and peer isolation. Student factors include low income of parents, reluctance to take out large loans to complete a baccalaureate degree or to pursue graduate study, and a job in addition to full-time study. Thus, both personal factors (to some degree) and institutional factors (to a greater degree) are responsible for the attrition of minority students in SME programs. When changes are made in curricular offerings or instructional strategies, dramatic improvement can occur in minority student achievement. When Uri Treisman, of the University of California at Berkeley, altered student learning through collaborative study, he dramatically increased minority student performance. African American and Hispanic students in the program outperformed both white and Asian students in the same course who were not in the program (Treisman 1985). Similar results were obtained by Henry Frierson with nursing and medical students at the University of North Carolina at Chapel Hill (Frierson 1989; see table 8), and by Lewis Kleinsmith with biology students at the University of Michigan (Kleinsmith 1987; see figure 1). Such curricular and instructional innovations seem particularly effective when applied to science-based courses of study and hold great promise for their replication in other subject areas. The most comprehensive minority-retention program, the Meyerhoff program at the University of Maryland in Baltimore County, included complete financial support as well as mentoring, peer support groups, and the prestige of being viewed as a

figure 1 Mean Biology Exam Scores at the University of Michigan. Data source: Kleinsmith 1987.

200 The Undergraduate Years plus One table 8 University of North Carolina: Nursing Scores 1989

Skill group

SAT–verbal SBE average

346 451

Comparison group 351 355

Source: Frierson 1989.

model and successful program by others on campus. Minority scholarship programs like these are of tremendous importance in boosting the academic performance and success of underrepresented students. But recent court decisions may very well prove to be the greatest impediment to both reuniting and awarding grants to minorities. The United States Court of Appeals, Second Circuit, in Podberesky v. Kirwan (956F. 2d J2 [1992]), found “race-based scholarships” awarded by the Banneker program to be unconstitutional, thereby placing these programs in jeopardy. (A more recent Supreme Court ruling that adversely affected these programs was Hopwood v. University of Texas [1996].) Nevertheless, the history of such scholarship assistance is long and reaffirms its importance in providing support to minorities in SME programs. Early African American applicants to engineering schools in the 1940s were provided with vouchers from the southern states, which were strictly segregated, to attend schools in the north (Wharton 1992). As early as 1892, schools such as the Massachusetts Institute of Technology graduated African Americans with degrees in engineering and technology who were subsidized by the southern states not out of generosity but in order to maintain segregation. But these vouchers proved how crucial financial aid was—and continues to be—in supporting minority completion of degrees. This situation continued until 1934, when Howard University became the first accredited African American school of engineering and architecture (established 1910), thus providing an alternative to the limited openings in the white schools. Eventually there were a total of six historically African American schools of engineering awarding about 40 percent of the degrees earned by African Americans and a substantial number earned by Hispanics, a process that continues to this day. “Colleges and universities have become more accepting of students who do not fit the mold and who are different from their customary alumni” (Wharton 1992:48).

The Effects of Faculty on Students The presence of minority faculty on college campuses is low and is not growing at a rapid enough pace (see table 9). Minority faculty in SME programs is even scarcer because fewer graduate degrees are earned in these disciplines. African Americans, Hispanics, and American Indians constitute only a moderate percentage of doctoral degrees (see table 10), resulting in a paucity of faculty, since some of these Ph.D.s will undoubtedly be attracted to industry rather than teaching positions (Bowen and Rudenstine 1992). This has various implications for mentoring, counseling, and career guidance.

Barriers to Minority Success

201

table 9 Total Faculty of Color (%)

Asian African American Hispanic American Indian

1980

1990

3.0 4.3 1.9 0.3

5.0 4.5 2.0 0.3

1991 5.1a 4.7b 2.2 0.3

Source: Minorities in Higher Education 1994. aHalf bHalf

are foreign-born. are in HBCU.

James Blackwell (1981) has highlighted several important roles for minority faculty: (1) demonstrating competence in subject matter; (2) showing a sincere interest in students as individuals; and (3) “intrusive advising” through frequent student contact to check on a student’s academic progress. Of course, majority faculty can perform some of these roles as well, but rarely with the ease and intimacy of minority faculty. This is particularly acute in the case of minority women, whose numbers are growing in SME programs but whose counterparts in the faculty are infinitesimal. This fact is crucial, as Blackwell has documented, since one of the greatest contributors to minority student academic success is the presence of minority faculty. Some researchers have found that majority faculty in engineering schools “has minimal contact with most students, and is usually even more removed from minority students” (Morrison and Williams 1993). This fact, coupled with low faculty expectations, can lead to poor academic performance and an overall feeling of isolation. On the other hand, programs that require strong faculty participation as a condition for program funding experience active and enthusiastic participation by majority faculty. Programs such as MARC (Minority Access to Research Careers) and GEM (National Consortium for Graduate Engineering Degrees for Minorities) have strong faculty components, with students working directly with faculty on major research projects (Malcom 1991). These programs do not isolate faculty, who have high expectations of students. The latter meet those expectations and produce exemplary work. Moreover, these programs are perceived as high status, central to the mission of the institution, and have tenured research professors as teachers. Thus, the entire university takes pride in them.

table 10 Doctoral Degrees in Engineering (%)

Asian African American Hispanic American Indian

1980

1991

5.8 0.9 1.4 0.2

9.4 2.2 2.4 0.3

Source: American Council on Education. Note: This table only considers degrees awarded to U.S. citizens.

202 The Undergraduate Years plus One

Structure and Function of Retention Programs The key to minority student retention is encapsulated in the following comment: The biggest and longest lasting reform of undergraduate education will come when individual faculty or small groups of instructors adopt the view of themselves as reformers within their immediate sphere of influence, the classes they teach every day. K. Patricia Cross, 1989

This statement reflects the belief of the individual faculty member that every person can learn, despite prior educational deficiencies and standardized test scores— indeed, can even excel. The fine points of effective retention programs must begin here. In order to understand retention and remove barriers, we must first ask ourselves why students leave college. Vincent Tinto (n.d.) has suggested that initially there must be a comprehensive student assessment system to collect data on every student who enters the institution. Such information should include “student ability, study skills, social background, educational and occupational goals and commitments . . . and pre-entry expectations about the quality of institutional life” (191). Information should be collected on students’ formal and informal relations both inside and outside the university. Some of these indices will be found to be quite strong, while others will be weak. When looked at in the aggregate, a pattern should emerge to serve as a guide for establishing an institutional retention program. Periodic reassessment will help the institution to improve its retention program. Second, institutions with effective retention programs often have pre-freshman bridge programs to build up a student’s skills in math and science before enrolling in rigorous college courses. Moreover, effective bridge programs should also include social activities; research indicates that a large number of minorities who fail in college “felt themselves to be very little, or not at all, part of the general campus life” (Tinto n.d.:191). An exemplary summer bridge program run by Richard Tapia at Rice University is called “Spend a Summer with a Scientist” (Medina 1993). The program brings minority students to the campus during the summer to assist a Rice faculty member with research. In 1990 the National Research Council named Tapia one of the twenty most influential leaders in minority mathematics education in the country. Another exemplary program is the NACME-sponsored Engineering Vanguard Program, piloted at Rice and Lehigh Universities. Reports of the results indicate a retention rate of 100 percent among current students. Good transitional programs such as these can go far toward assuring the college success of minority students in SME. Innovative retention programs share many of the characteristics of successful bridge programs. According to NACME (Morrison and Williams 1993), such programs, when successful, tend to exhibit the following critical variables necessary to improve retention: 1. institutional leadership and commitment to student success 2. recruitment, selection, and retention practices designed to improve the “fit” between the student and the institution 3. adequate financial aid 4. an academically supportive faculty with high expectations for student success

Barriers to Minority Success

203

5. students experiencing academic accomplishment in the classroom 6. a supportive student community that expects students of color to succeed 7. comprehensive academic support services (particularly during the freshman year) that do not stigmatize participants 8. an academic alert system designed to discover academic problems early NACME has developed and implemented precollege and college retention programs that meet these criteria. In addition to the Engineering Vanguard program, the Corporate Scholars Program has produced retention greater than 80 percent and the NACME Diversity Seminars have created significant change in campus climate (Campbell 1992b). NACME research has also focused on which retention efforts are especially successful within minority engineering programs (MEPs). One study found that despite “the twenty-year history of MEPs, [they have] not resulted in an appreciable increase in the retention rate of minorities in engineering” (Morrison and Williams 1993:10). Therefore, just having a MEP on campus does not ensure its success. A study of the most successful MEP programs (Morrison and Williams 1993) has shown that they all stress the following features: 1. primarily high school outreach 2. no special admissions policies 3. tend to combine course work with skills workshops and orientation activities 4. provide study centers 5. likely to have sufficient tutors 6. high level of university finding 7. tend to have full-time director 8. high perception that faculty is supportive What was most important was the finding that university efforts to improve retention tend to focus exclusively on students. This approach does not directly address institutional obstacles, cross-cultural communication barriers, low faculty expectations, etc.—factors that contribute to the high attrition of minority students. As a result, even if an MEP program is effective, because it deals largely with students, it does not bring about permanent change in the institutional environment. (Morrison and Williams 1993:10)

The fundamental point is that instead of always looking at minority student flaws, institutional barriers must be addressed or the students will always face barriers to success that are endemic to the institution.

Women in SME Programs Although few gender-disaggregated studies have been done, when talking about “minority” underrepresentation in SME programs, minority women encounter special problems that explain their limited presence. All minority women, including Asian women, are underrepresented in SME programs (except in the biological and life sciences). SME programs have traditionally been considered “tough, male” pro-

204 The Undergraduate Years plus One table 11 Minority Women in Engineering (B.A.)

African American Hispanic Asian American Indian

1981

1991

429 131 367 22

976 433 1,338 33

Source: American Council on Education 1993.

fessions. Until recently, women were not actively recruited; indeed, they were actually discouraged from entering them (see table 11). Fortunately, some but not all of these sexist and racist barriers have been removed. “In academe, entering women doctorates earn lower salaries than males at every age and status level. The gap between male and female scientists’ salaries widens as their years of experience increases” (Collins and Maytas n.d.:119). Minority women scientists not only suffer lower pay but slower advancement, fewer rewards, less recognition, and less job security. In addition, they often suffer the negative attitudes of male coworkers and colleagues. These inequities can result in professional isolation and lack of recognition as a legitimate partner in peer groups. Attrition is a particular problem for minority women in engineering because their enrollment is small to begin with (see table 12). Minority women are doubly handicapped in SME programs not only due to their gender but their race as well. As a consequence, they are the most underrepresented group in the fields of SME. Therefore, minority women must be actively recruited in these fields, with special attention given to their self-esteem, nurturance, and mentoring. Moreover, minority women need much more research devoted to their representation and success in SME programs. They must be disaggregated from minority men as well as from white women since their experiences are unique (Clewell and Anderson 1991).

Conclusion To summarize, this chapter has addressed four main issues affecting minority success in SME programs: (1) inadequate precollegiate education, particularly in science and mathematics courses, which precludes entry into competitive and rigorous programs at the collegiate level; (2) noncollegiate career choices (armed services, proprietary schools, etc.), which siphon off potential enrollees in SME programs; table 12 Attrition of Minority Engineers, 1991 (%)

Minority men Minority women Source: Campbell 1992a.

Freshmen

Graduates

11.0 4.2

5.3 1.9

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(3) barriers at the undergraduate level that inhibit entry to engineering and science programs (low SAT scores and grade-point averages); and (4) poor retention once admitted to these programs (isolation, lack of mentoring, etc.). At the same time, it was demonstrated that innovative programs have been able to overcome these barriers at both the primary (Sizemore 1988) and the collegiate levels (Kleinsmith 1987). However, programs are few in number and are not implemented because of the traditional focus and “corporate climate” of the higher education structure. Special attention must be given to the underrepresentation of women in SME programs, which have traditionally been viewed as a male domain. Although women have made encouraging gains in recent years, they still require counseling and mentoring to encourage them to enter and remain in these male-dominated fields. Further research is needed to isolate those factors that directly affect minority success in SME programs. For example, not enough is known about the effect of teacher expectation levels on student performance; or the lack of a coherent science experience in U.S. schools, as compared with other developed countries. We need more data on sex, race, and ethnic groups in order to be more sensitive to differences within groups. We need to determine the cost to society of not educating minorities in SME. Much more research is needed to better understand successful retention programs and what makes them work. References Blackwell, James E. 1981. Mainstreaming Outsiders: The Production of Black Professionals. General Hall, Inc. Bowen, William G., and Neil Rudenstine. 1992. In Pursuit of the Ph.D. Princeton, NJ: Princeton University Press. Bureau of the Census. 1993. Current population reports: School enrollment. Campbell, George, Jr. 1992a. The gender gap in minority engineering education. NACME Research Letter (NACME):3. ——— . 1992b. Testimony to the President’s Council of Advisors on Science and Technology. Typescript. Carter, Deborah J., and Reginald Wilson. 1990. Minorities in Higher Education: Ninth Annual Status Report. ——— . 1993. Minorities in Higher Education: Twelfth Annual Status Report. Clewell, Beatriz Chu, and Bernice Anderson. 1991. Women of color in mathematics, science and engineering: A review of the literature. Center for Women Policy Studies:3. Collins, Mildred, and Marsha Lakes Maytas. n.d. Minority women: conquering both sexism and racism. Typescript. Farley, Reynolds, and Walter R. Allen. 1987. The Color Line and the Quality of Life in America. Russell Sage Foundation. Feagin, Joe R., and Melvin P. Sikes. 1994. Living with Racism: The Black Middle-Class Experience. Beacon Press. Fleming, Jacqueline, A. Ramona Brown, and Charles B. Watkins. 1992. Quantitative assessment of an effective minority engineering program for urban students. New York: Motivation Research Corporation, CUNY. Frierson, Henry. 1989. Intervention can make a difference: The impact on standardized test and classroom performance. Typescript.

206 The Undergraduate Years plus One Hexter, Holly. 1988. Joining Forces: The Military’s Impact on College Enrollments. American Council on Education, Division of Policy Analysis and Research. Justiz, Manuel J., Reginald Wilson, and Lars Bjork, eds. Minorities in Higher Education. American Council on Education–Oryx Press. Kleinsmith, Lewis. 1987. A computer-based biology study center: Preliminary assessment of impact. Academic Computing 2(3): 32–33. Malcolm, Shirley M. 1991. More than market forces: Policies to promote change. In M. L. Maryas and Shirley Malcom, eds., Investing in Human Potential. American Association of the Advancement of Science. Medina, David D. 1993. I Must Help. Sallyport (October/November): 16–20. Morrison, Catherine, and Lea E. Williams. 1993. Minority engineering programs: A case for institutional support. NACME Research Letter (NACME) 4(1): 10. NACME. 1994. Rice University Welcomes Multicultural Team. NACME News (May):1. Paul, Faith. 1993. State higher education systems and bachelor’s degree attainment. National Center for Academic Achievement and Transfer 4(4): 2. American Council on Education–Oryx Press. Rendon, Laura, and Amanoy Nora. 1994. Clearing the pathway: Improving opportunities for minority students to transfer. In Manuel J. Justiz, Reginald Wilson, and Lars Bjork, eds., Minorities in Higher Education. Washington, DC: American Council on Education–Oryx Press. Sizemore, Barbara. 1988. The Madison Elementary School: A turnaround case. Journal of Negro Education 57(3): 243–256. Suzuki, Bob. 1994. Higher education issues in the Asian-American community. In Manuel J. Justiz, Reginald Wilson, and Lars Bjork, eds., Minorities in Higher Education. Tinto, Vincent. n.d. Leaving College: Rethinking the Causes and Cures of Student Attrition. Chicago: University of Chicago Press. Tresiman, Uri P. 1985. A study of the mathematics performance of black students at the University of California, Berkeley. Ph.D. diss., University of California–Berkeley. Wharton, David E. 1992. A Struggle Worthy of Note: The Engineering and Technological Education of Black Americans. Greenwood Press. Wilson, Reginald, and Deborah J. Carter. 1988. Minorities in Higher Education: Seventh Annual Status Report.

richard c. richardson, jr.

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richard c. richardson, jr.

The Role of State and Institutional Policies and Practices In this chapter I plan to focus on three points. First I’ll briefly examine the role of state policy in shaping institutional policies and practices that impact on minority access and achievement. I’ll use community college articulation and transfer as “exhibit A” to compare the effects of state policies that largely exhort institutions to do a better job of negotiating individual articulation agreements versus state policies that establish parameters for the transfer process among all public institutions and monitor results. Second, I’ll examine in greater detail the need for coordination among the interventions an institution devises to improve minority participation, retention, and graduation; and regular institutional practices, especially as these apply to teaching and learning. I will also suggest that although we have done quite a good job of implementing those interventions that focus on getting more students into college and helping them survive the freshmen year, we have been much less successful in getting faculty to adjust their practices in order to take advantage of the better-prepared students we are providing to them. The lack of serious faculty engagement in addressing the issues of minority achievement is particularly acute in research universities, where a disproportionate amount of “brain power” in math, science, and engineering is centered. Finally, I will comment briefly on what I see as some of the challenges in helping less well prepared students excel in engineering, math, and science, which may be quite different from helping their counterparts succeed in other disciplines and professional fields.

A Model of Institutional Adaptation As a frame of reference for these comments, I refer to a model that was designed to explain why some institutions have better participation, retention, and graduation rates than others (see figure 1). I use the model to underscore the extent to which interventions aimed at improving academic achievement are interrelated with each other and with the larger state and institutional context. Too often, when we talk about the results a particular intervention has achieved, we fail to mention the web of state and institutional practices in which the intervention is embedded and the impor-

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figure 1 A Model of Institutions’ Adaptation to Student Diversity. Student diversity has three major dimensions: (1) preparation, (2) opportunity orientation, and (3) mode of college-going. African Americans, Hispanics, and American Indians share these dimensions with other groups but are distributed differently as a function of historic discrimination and socioeconomic status. Note: Model modified January 16, 1990. Data source: Author

tance of that web in making the intervention work well. The model was developed from case studies of public universities in the United States that were leaders in their respective states, as well as in the nation, in enrolling and graduating students from Native American, African American, and Hispanic heritages (Richardson and Skinner 1991). It was subsequently tested empirically on 144 institutions in 10 states (Richardson 1991) and then used as a guide for comparable case studies of university efforts in South Africa to accommodate blacks (Pavlich, Orkin, and Richardson 1995). The model suggests that when institutions are confronted with internal or external pressures to improve participation for underrepresented groups, they become reactive (Stage 1) by emphasizing recruitment, financial aid, and alternative admission standards. The more diverse students admitted through these interventions experience high attrition rates because of problems in negotiating academic environments designed with a different clientele in mind. The strategic response (Stage 2) is characterized by more comprehensive and better coordinated interventions designed both to change students so that they became a better match for the institution (outreach, transition programs, mentors) and to change the institution (environment) in order to make it less difficult to negotiate for students who differ in level of preparation, career objectives, or skin color from those traditionally served. In the more advanced response (Stage 3), institutions use

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adaptive strategies (assessment, learning assistance, pedagogy) to alter the learning environments they provide for a more diverse student clientele. Moving institutions through the three stages requires committed leaders who use strategic planning to shape the discrete responses generated in Stage 1 into a coherent system to help students cope and to change institutional practices in Stage 2. In addition, institutional leaders support longer-term efforts to diversify the faculty and to provide incentives and support for the learning improvement strategies that characterize Stage 3. Movement through the various stages is not automatic, nor is it irreversible. Institutions can stay in Stage 1 or Stage 2 long after the problems of these stages might have been recognized and addressed. Institutional leaders are aided in addressing equity concerns where consistent state policies support equal educational opportunity as a priority, provide support for financial aid and proven interventions, and mandate planning and accountability. Most historically Anglocentric institutions in the United States remain focused on Stage 1 strategies, which seek to improve participation, and those Stage 2 strategies that either seek to change students in order to make them a better “fit” for an unchanging institution or to buffer them from the worst aspects of an institution that would really prefer to serve students like the ones they used to get. Few have addressed the Stage 3 interventions that require serious faculty involvement and significant curricular and pedagogical reforms. I was surprised to find more Stage 3 adaptations in math, engineering, and the sciences in the South African study than in the universities I visited in the United States (Richardson, Orkin, and Pavlich, 1996). The model referred to earlier summarizes the range of state policies, administrative actions, and campus strategies that impact participation, retention, and graduation according to the stages they affect and the outcomes they most directly influence. I’d like to turn to the state policy arena as it affects community college transfer.

Community College/University Articulation Scholars and practitioners alike have noted the importance of articulation agreements negotiated between pairs of two- and four-year institutions. Far too often four-year institutions avoid inner-city community colleges, where minorities are concentrated, in favor of whiter and more affluent suburban community colleges. Extensive evidence of this practice was documented in a study (Richardson and Bender 1987) of such cities as Chicago, Detroit, and Dallas. In the absence of full cooperation from neighboring public universities, those community colleges that serve the largest numbers of minority students are powerless to implement any proposed solutions. Some states, most notably California and Florida, have narrowed the range of public university discretion in the articulation arrangements they develop with public community colleges. Statewide course-articulation numbering systems that guarantee students will receive degree credit at any public institution for specified courses, and state rules about the general education core, common course-numbering systems, institutional liaisons, and an external-appeal process for students who believe they have been inappropriately denied credit all provide a firm foundation upon which individual articulation agreements can be based without reinventing the wheel every

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few years. They also broaden the range of institutions to which students can transfer with full assurance of fair treatment. For anyone who thinks goodwill among institutions is sufficient to offset the need for state policy, the experience of Pennsylvania is enlightening. For nearly thirty years the state university system and the community colleges held regular meetings to deplore the transfer problems students experienced when moving from, say, Harrisburg to Indiana University of Pennsylvania. The legislature finally got fed up and threatened to impose its own articulation policy, which would have stated that everything taken in a community college would be accepted for degree credit at a state university. The two systems subsequently adopted a statewide articulation agreement and established the necessary machinery to police it, based on adaptations of the policies already in use in Florida and California. Now the legislature is happy and so are the students. In a study of urban community colleges in eight major U.S. cities (Richardson and Bender 1987), it was found that the percentage of transfer students selecting majors in engineering and the hard sciences ranged from 7 to 26 percent. The institution with the highest percentage in these majors also had the largest discrepancy between the proportion of African Americans in its student body (57 percent) and the number transferring (32 percent). Data suggested an inverse relationship between the emphasis placed on math and science by a community college and the probability that its African American students would transfer. The results also suggested that inner-city institutions could make choices about their curriculum and that such choices were reflected in a student’s career choices.

The Role of Numbers in Driving Institutional Practice Even more than state policy, numbers drive the institutional adaptation process. Among the best adapted and most successful institutions in the previously mentioned study were the University of Texas at El Paso and Cal State Dominguez Hills, where multicultural student populations made it clear to faculty and administrators that institutional survival was inextricably bound up with the success of their African American, Native American, and Hispanic students. In such institutions there were no “minority” students; the focus was on helping all students succeed through effective teaching and learning practices. As long as institutions are able to restrict the enrollment of minority students through inflexible admission practices and inadequate retention of those who do enroll, they will need special programs to change students and buffer them from unfriendly environments. The days of such practices seemed numbered, both in a nonracial South Africa and in a growing number of U.S. states and cities, until recent actions by the Board of Regents for the University of California reopened the affirmative action debate in response to the demise of a program that has been widely characterized as exemplary. Number are, of course, the crux of the problem in the fields of engineering, math, and science. We need more African American, Native American, and Hispanic students to increase the pressures for curricular and pedagogical change. Because advanced knowledge rests on a base of prerequisite competencies in the hard sciences, Stage 3 interventions are crucial. Not many are in evidence, partly because of the research priorities in many of the institutions where programs in the hard sciences

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are situated and partly because the low numbers of minority students permit these programs to avoid necessary reforms to make them truly responsive to a more diverse clientele. In the hard sciences and in medicine, effective programs • provide students with more time to master the same material • use socialization experiences primarily to contribute to academic objectives rather than as ways of buffering the student from the campus environment • involve faculty members in curricular reform to articulate bridge programs involving advanced work • emphasize changes in pedagogy to increase student success rates In general, programs in the hard sciences tend to be much more disciplinespecific and faculty-driven than in the humanities and the social sciences. In fact, one of the reasons why math and science transfers from community colleges are less common than in other disciplines has to do with the extent to which their developmental programs tend to be based on social science models. In both South Africa and the United States, faculty members in different disciplines invent and accept different strategies for responding to diversity. We need to find ways of encouraging more faculty in the hard sciences to emulate the work of Uri Treisman and Jaime Escalante. At the same time, we need to pay careful attention to the kinds of students a particular intervention has been organized to serve. The Treisman lab at Berkeley served a group of students that, by any measure, were among the best prepared students in the country. They also happened to be African Americans studying in an extremely competitive environment. The Meyerhoff program is designed to help the University of Maryland in Baltimore County (UMBC) compete successfully for students against ivy league institutions. It seems unlikely that either of these programs would be suitable for the colleges that most average minority students attend.

The Role of Funding Incentives Numbers are one way of encouraging faculty to become more involved in Stage 3 strategies. Recruiting a more diverse faculty and emphasizing professional development are also crucial, as are strategic planning and administrative commitment. I found one additional strategy in use in South Africa that also seemed to make a difference. Institutions received about a third of their governmental appropriations based on graduation rather than enrollment. The South African database also keeps track of course completions as a percentage of course enrollments by race and ethnicity. Such practices foster a higher level of faculty accountability than do comparable arrangements in the United States. References Pavlich, G. C., F. M. Orkin, and R. C. Richardson. 1995. Educational developments in post-apartheid universities: Framework for policy analysts. South African Journal of Higher Education 9(1): 65–72. Richardson, R. C. 1991. Promoting Fair College Outcomes: Learning from the Experiences of the Past Decade. Denver, CO: Education Commission of the States.

212 The Undergraduate Years plus One Richardson, R. C., and L. W. Bender. 1987. Fostering Minority Access and Achievement in Higher Education. San Francisco: Jossey-Bass. Richardson, R. C., D. A. Matthews, and J. E. Finney. 1992. Improving State and Campus Environments for Diversity: A Self-Assessment. Denver, CO: Education Commission of the States. Richardson, R. C., F. M. Orkin, and G. C. Pavlich. 1996. Overcoming the effects of apartheid in South African universities. Review of Higher Education 19(3): 247–266. Richardson, R. C., and E. F. Skinner. 1990. Adapting to diversity: Organizational influences on student achievement. Journal of Higher Education 61 (Sept./Oct.): 485–511. ———. 1991. Achieving Quality and Diversity: Universities in a Multicultural Society. Washington, DC: ACE/Macmillan.

vincent tinto

Reflection on the State of Research: What Next? Reginald Wilson has very effectively summarized what we know about the underrepresentation of minorities in science, mathematics, and engineering at least as it is observed in the undergraduate years plus one. His review makes clear that we have had a good deal of research on both the scope of underrepresentation in undergraduate education and its various individual and institutional roots. In this regard, it is fair to say that our understanding of the nature and causes of underrepresentation has become increasingly detailed over the past ten years. It might even be said that on a number of topics we have had enough research and that action rather than more study is now called for. The same, of course, cannot be said for the graduate years. Nevertheless, there remain a number of areas where additional inquiry is warranted, where our understanding of the causes and possible cures for underrepresentation is far from complete. I would like to address several areas where further inquiry, or at least clarification, is required. I will limit myself to those studies that focus exclusively on the institutions, colleges and universities, and events within institutions that shape student persistence. This is not to say that events that have taken place before entry as well as what one anticipates might occur after graduation are not important. Rather, my examination is dictated by the task before me, namely, to focus on the undergraduate years and the additional research needed to further advance our goals.

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First, our research needs to better explain how race, gender, social class, and ability differentially relate to the issue of underrepresentation. For instance, as regards differences in college persistence generally, we know that nearly two-thirds (or nearly 15 percent) of the overall difference in rates of college completion between Anglo-American and African American students (roughly 22 percent) can be attributed to aggregate differences in the social class backgrounds. Among persons of similar social class backgrounds (e.g., young people from college-educated families), differences between the two racial groups average between 5 and 9 percent. Though by no means insignificant, such differences are not on the same scale as those for the two groups generally. At the same time, those differences vary considerably among persons of differing ability and gender. The problem is not that we are unable to describe those differences in an aggregate sense. (As Reginald Wilson and Shirley Vining Brown have noted, in separate chapters, we have already done so.) Rather, it is that we have not yet had the types of systematic, long-term inquiries that would enable us to sort out the complex interplay of those factors over time and enable us to better understand how and why those attributes differentially shape persistence. The need to better sort out these differences is not just a matter of clarifying our research conclusions. It is also a question of policy. Our ability to construct and target effective policies hinges upon our ability to better understand how these differing attributes come to shape differences in entry to and persistence within the sciences, engineering, and mathematics. Of course, these attributes are not so easily disentangled in the real world. We know only too well how they have become intertwined in our society. Nevertheless, from a research perspective, our studies of these matters must help practitioners discern to what degree their programs and policies must be directed toward issues of differential social class background—such as economic well-being and quality of prior educational preparation—as opposed to issues of race, such as campus climate and racism. Second, while it is clearly the case that research on underrepresentation is plentiful, much of this research has not been sufficiently long-term or systematic in nature but rather short-term and piecemeal. If we wish to sort out the varying ways in which differing attributes have shaped persistence over time, we will have to commit ourselves, researchers and funding agencies alike, to long-term systematic longitudinal panel studies of persistence. Unfortunately, such studies and their concomitant funding are difficult to come by. Third, to follow up a point made by Shirley Vining Brown, we also need to better understand the differences between attrition as an institutional phenomenon (i.e., dropouts) and field switching within institutions. Again, though we have accumulated good descriptive data on patterns of field switching, we have not yet been able to explain why such switching occurs and whether the data differentiate between those who switch from those who leave higher education altogether. Once more the need for systematic, long-term study is apparent. Fourth, we need to move away from the purely academic mode of research— which seeks to isolate the manner in which attributes of students, faculty, and institutions shape individual entry and persistence—to a more action-oriented type of

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research that focuses more fully on the complex interplay among students, faculty, and institutions that underlies programs that are successful in raising self-esteem among many students. In other words, we have to study what works in a manner that enables us to learn how to be more effective over time and to teach others how they too can be successful in promoting the achievements of minority students. In the real world of program functioning, this also means that our research studies must be more broadly defined than they have been in the past and must employ a variety of research methodologies in their analysis of program success. Too often our studies have been narrowly constructed and have relied on but one mode of analysis, most commonly quantitative in nature. Yet, as the work of Gail Thomas, Beatriz Chu Clewell, and Willie Pearson Jr. demonstrates, the use of multiple methods of inquiry that include qualitative methods, especially when applied in the casestudy mode of inquiry, yields insights that no single method alone can achieve. Let me add two further observations. First, even though it is terribly important that we have more qualitative, ethnographic studies of student experiences in college, those studies alone will not suffice to meet the challenges before us. It simply is too easy for skeptics to dismiss qualitative research studies, especially those of individual institutions or programs, as nongeneralizable. We need to blend the merits of quantitative research, especially longitudinal panel survey studies, with those of ethnographic studies that reveal the complexities of human experiences and the meanings individuals attach to those experiences. Second, we need to cross disciplines and methodologies to form collaborative research teams that draw upon the skills of several researchers working in consort. In other words, we must work together in a fashion that has heretofore been the exception rather than the rule in academic practice. Finally, our research must recognize what practitioners have long understood about the “secret of student persistence,” namely, that student persistence arises from successful learning, and that successful education, not retention, is the root of successful retention efforts. What this means for future research is that we have to pay greater attention to those events within colleges and universities that shape student learning. Among the things we should look at, several come immediately to mind. First, we need to look more carefully at the classroom experience of minority students and the events that shape successful learning outcomes in that setting (e.g., collaborative versus competitive, isolated learning). Second, we have to look at issues of faculty teaching and the types of pedagogies faculty employ within classrooms to promote student learning. Lest we forget, we in higher education are the only faculty in education, from kindergarten through college, who are neither trained nor certified to teach our students, least of all minority students. I sometimes wonder why we call it “higher education.” At the same time, as contrasted with an increasing number of elementary and secondary schools, the pedagogy of higher education is still dominated by lectures and the narrow view that students learn best by themselves and in modes that require “solo performance.” Yet we know from research conducted by the National Center on Postsecondary Teaching, Learning, and Assessment on learning communities and collaborative learning, and research on group learning by Uri Treisman, among others, that all students learn better together and that “at risk” students (for lack of a better term) are particularly well served by those pedagogies. Unfortunately, our research—indeed, our practice—in this regard has been quite

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weak. Perhaps it is too painful or too threatening for us to consider ourselves, our practices, and our pedagogies as part of the “problem.” The question of the causes of the differential rate of minority persistence in math, engineering, and science is not the only question about learning and persistence that we must address. We must also ask why it is that attrition is so high in those fields for all students. It is my view that an answer to that question will begin to redress the underrepresentation of minorities in math, science, and engineering. Once we open up the conversation about teaching and pedagogy, we necessarily raise important questions about the nature of classroom authority and how classroom practice enables all students to have a voice in the dissemination of knowledge within the classroom. All of the above is simply another way of saying that our research must be tied to our practice in a multifaceted way that will ensure success. Indeed, in any evaluative process our research must be part and parcel of our practice. In my view, we have for too long separated research from practice, assuming that the former would eventually shape the latter. Unfortunately, our students cannot wait for researchers to resolve their continuing debates about appropriate theories of underrepresentation. It is to that end that any future research must be directed.

william yslas vélez

University Faculty: Priming the Pump or Lying in Ambush? There appears to be great misconception about the role of university faculty. It is not to communicate to students the technical aspects of our subject; our role is to communicate a point of view. My comments are not meant to be universal but rather are directed to the mathematics community. I have spent most of my academic life in that insular cocoon called research. For me, the most wonderful aspect of being a researcher is that feeling of being totally lost, without a clue as to where the next idea is coming from. At times like these, a researcher goes through an agony. Should the problem be abandoned or pursued for yet another day? And then the fog lifts and we are flooded with those sought-after insights. Along the path we find obstacles, but persistence or inner strength allows us to continue. What joy! That is my point of view.

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If these observations seem too focused on the discipline of mathematics, consider this: Suppose that we could increase to a flood the number of high school students who are interested in pursuing careers in the sciences, engineering, and mathematics. When these students arrive at college, they have to pass through calculus courses. And it is here that we manage to bore them to death. Can it really be that the subject is so dry and boring? Nothing could be further from the truth. Mathematics is experiencing a flowering. The last forty years have seen some of the most important problems of mathematics being resolved. Centuries-old questions have fallen to the juggernaut of modern mathematics. But, as this juggernaut has pulverized the fortifications of countless problems, it also appears to have crumpled the career choices of many of our students. Yet, mathematics is basic to modern day society. The language of mathematics is used to phrase the basic laws of nature, to predict the movement of arctic storms across the continents, to model the flow of blood through our veins. Its importance is so pronounced that young people who are ignorant of mathematics will not be able to participate in today’s technological world. It is an indictment of today’s society that while mathematics has blossomed in our universities, our students are avoiding mathematics-based careers. Some of the blame lies within our profession and with the social workings of our departments. Mathematics faculty must begin to talk to students. Among the various methods that I have used over the years, one rule of instruction is to spend one day in the semester presenting some aspect of mathematics that is not covered in the syllabus and that shows my own fascination and interest in mathematics. We should not be afraid to show the emotion we have for our subject. All faculty should make it a point to have a personal conversation with their students, at least once during the semester. It doesn’t take much: What are you majoring in? What courses are you taking? Have you been enjoying them? These are very simple questions, but few faculty members take the time to engage their students in even this rudimentary conversation. The tendency for faculty to distance themselves from their students may be an outgrowth of the cultural behavior we learned to gain entrance into the profession. It is time to change this behavior. Over the years, I have tried various forms of interaction with students. Sometimes I require students to read and discuss with me some chapters from a book on the history of mathematics. The content of these readings often encouraged students to continue their studies in mathematics. Some activities go beyond casual conversations and are aimed at increasing the number of students, especially minority students, who choose careers in the sciences. The program might be described as aggressive advising. At the beginning of the semester I obtain a list of minority students enrolled in the calculus sequence, usually about 200 students. The students come in to see me, I go over their schedule of courses with them, ask them about career goals, and tell them about my own career goals and the many activities I am involved in. I may also tell them about some of the problems I have encountered in going through the system. I try to impress upon the students how important mathematics is to their future and also mention the possibility of their becoming mathematics majors. This can be shocking to students who never realized that such a career existed, much less that they might have that option. The following are examples of typical conversations I have had with students.

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Juana: I asked Juana the typical questions. Juana was taking first-semester calculus and first-semester chemistry during her second semester on campus. When asked what her major was, she said that she was undecided. I wrote down that she was a mathematics major. She said, “What!” I told her that anyone who is taking calculus and is undecided automatically becomes a mathematics major and I become their adviser. I took out the form that would make her a mathematics major and told her that I would fill it out later. She was a bit in shock. I asked Juana what she took during the first semester. She said calculus and chemistry but told me she had failed both courses. What happened? Well, Juana has a problem of procrastinating. It is hard for her to get going. She got a little sick last semester and then sort of gave up. How is this semester going? Procrastination was rearing its head again. I told her that she had two options. Option 1: I recommended several relatively easy courses she could pass without having to work too hard. If she wasn’t going to decide to work hard, these courses would allow her to coast along in school for a while until she was able to make up her mind to start dedicating herself to work. In the interim, the university wouldn’t kick her out because of bad grades. Option 2: She could become a mathematics major with me as her adviser. I would work with her to develop a challenging program of study. Besides the mathematics courses that she would be taking, I would explore with her various other courses to determine where her interests might lie. I emphasized that my role was simply to be her adviser. It was up to her to make the decisions. If she did not follow my advice, I would not be upset. I would hope that she would continue to allow me to help in developing her program of study. I also told Juana that I lost lots of mathematics majors to other disciplines. Sometimes, during this process of exploring other areas, a student finds some other subject more to her or his liking. When this happens I feel that I have helped the student to find the right subject area and to take more mathematics courses than the average student. What could be better!

This conversation took perhaps twenty minutes. Would the average mathematician have had this conversation with a student who had failed the first course in calculus? Why not, if that student is courageous enough to try it. Would such a student go on to graduate studies in mathematics? Many mathematicians believe in their hearts that advanced mathematics is only for those students who have Ph.D. potential. If the student does not want to study mathematics for the sake of doing research in mathematics, then the professor is wasting his/her talent on that student. This I believe is a very typical attitude among my colleagues. Wendy: Wendy is a first-year Navajo student who earned a 3.8 GPA her first semester. She wanted to study computer science. She had taken calculus and the first course in computer science during her first semester, and she was presently enrolled in second-semester calculus and the second course in computer science. Although she was doing well in her technical courses, she was earning a D in sociology 100. I advised her to drop it. She told me that she would lose her scholarship from the Navajo nation if she did so. I disagreed with her. Then she told me that her mother would not be happy with her if she dropped a course. She said that her mother had recounted a story about one of her cousins who had dropped out for a semester and had never

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returned to college. I told her that I did not want her to drop out of college, only to drop this one course so that the grade would not adversely affect her GPA. I offered to call her mother right there and then and explain why I was recommending that she drop the course. As to her scholarship, I told Wendy that I would call the Navajo nation to find out exactly what the rules were. After speaking with the administrator, I made certain that Wendy understood the rules that would allow her to make a decision. This meeting left the student with the feeling that a faculty member at this large, impersonal university actually cared about her academic progress. She later asked me to advise her roommate, who is also from the Navajo nation. I told her that I would be delighted to see her. Before the student left, I explained that she could become a mathematics major and still study computer science if she became disheartened with her computer science curriculum. I am very aggressive about encouraging students to choose mathematics for their major, not because I am interested in having them become mathematicians but because I care about their welfare. As their adviser, I have the opportunity to work with them. These efforts are showing results. In 1993–94, I had six Hispanic advisees graduate with bachelor’s degrees in mathematics, and the next year eight more; in 1995–96, I expected another ten such students. This last group included one African American student and one Yaqui student—the first member of the Native American nation that resides in Southern Arizona and Northern Mexico to receive a degree in mathematics. I now have thirty-five minority advisees in mathematics. I would like to make the following recommendations to mathematics faculty: • It is the responsibility of mathematicians to convince the community of the necessity of studying mathematics. • Departments have to find a way to evaluate and encourage good teaching and good outreach efforts. • Many research mathematicians believe that advanced mathematics is useful only for those students who want to pursue careers as mathematicians; professional mathematicians also need to be educated about the discipline’s usefulness. • Precollege teachers attend in-service workshops to increase their knowledge of the field. Such methods might be applicable to our university communities. It would be an opportunity to communicate the knowledge we create. It is here that we need lots of in-service help. • Let’s talk to our students. Our contact with students should serve to motivate them to further studies. We, as a community of scholars, should begin placing as much emphasis on communicating mathematics as we do in creating it. If we are willing to take taxpayers’ dollars to support our studies, we should also be willing to give their children the desire to be mathematically literate. It really is in our best interest to do so.

antoinette torres

Barriers Rethinking to Minority theSuccess Model 219

antoinette torres

Rethinking the Model A variety of strategies has been used to increase the participation of underrepresented groups in engineering programs and a set of model programs developed to provide academic enrichment and improve the curriculum. These programs, generally known as Minority Engineering Programs (MEPs) share such common features as financial support, admissions intervention, summer orientation, clustering of students, the development of a sense of academic community, and prematriculation activities. After twenty years of program intervention and support and the expenditure of millions of dollars, these programs have achieved mixed results. Underrepresented groups are still underrepresented (comprising only 9.2 percent of the graduates in engineering in 1995) and continue to be at risk in our colleges of engineering. It is time to rethink our model. While model components provide for academic and social integration, reports indicate that the academic success and intellectual and professional development of students are put at risk by what students perceive as disinterested professors, uninspiring and confusing courses, unwelcome departmental environments, and lack of competent advising and guidance. It has been said that the success of these model programs is idiosyncratic. Is the success of minority engineering programs really just a result of an individual’s (i.e., program director’s) leadership style, a temperamental peculiarity? Although successful minority programs share such elements as admissions (affirmative-action) intervention, summer orientation, and clustering and academic workshop support, they do not always share common successes. By taking a closer look at how we define these interventions, disparities in implementation from one program to the next will emerge. Admissions. How have we profiled potential for success? It is generally agreed that there is no significant correlation between high school grade point average (GPA) and SAT achievement, on the one hand, and success in engineering, on the other. Many of us can point to success stories of students who were told they would never “make it” yet still beat the odds. GPA and SAT scores are not great indicators of potential for success in engineering, yet we continue to rely on them as critical filters for admission into engineering programs. Ironically, critical admissions data do not inform our interventions in meaningful ways to ensure student success. The assumption is that the GPA and SAT tell us something about mathematics competency for a particular course taught by a particular instructor. Given that mathematics has been identified, in the words of Uri Treisman, as an “enabling force and critical fil-

220 The Undergraduate Years plus One

ter for careers in science and engineering,” recognizing that mathematics is critical to every field of study in engineering, and assuming that grades in introductory (firstyear) mathematics courses are a good predictor of subsequent achievement, it behooves us to better assess (1) the prerequisites and expectations of mathematics faculty in order to ensure that students possess the competency to do well, and (2) the patterns of academic achievement in gateway courses, by ethnicity and gender, to ensure that our students have the wherewithal to excel. One must also determine when and how, in the admissions cycle, we should evaluate salient qualities known to contribute significantly to the success of our students, qualities like intrinsic interest in and connection to the field, ability to work hard, tolerate frustration, and assume responsibility. Summer orientation. The institutional environment contributes significantly to the achievement of targeted students. Summer orientation programs are designed and implemented to introduce students to the climate and culture of the institution. Too often, though, they either “remediate” student skills or rehearse fall-semester courses. How do we instill in students an enthusiasm for problem-solving and a sense of high expectations and personal achievement, as opposed to a mere desire for praise? How do we translate high school math and problem-solving skills for the university level? How do we develop in the student a level of sophistication and facility with the ideas, principles, and methods encountered in the college environment? As we develop our summer programs, what attitudes and beliefs determine program design? Do we operate in the belief that our students can contribute in significant ways to the intellectual discourse within the classroom and the department? Do we believe and know that they can be valued and respected in a culture of heurisitic inquiry? Clustering and academic support. Treisman has also said that undergraduate math and science courses have traditionally been the “burial ground for the aspirations of minority students in engineering.” Academic workshops are designed to encourage student collaboration on challenging mathematical and scientific problems and to develop problem-solving abilities. They have traditionally relied on skilled facilitators who are experts in the subject and are trained in group dynamics. Workshops rely on student cooperation and communication, stressing facilitated learning and group dynamics. Since students are not trained to be effective partners in this educational process, it is no wonder that such workshops have met with limited success. How can we nurture the concepts of collaboration and teamwork? How can we enable small groups to develop an intellectual focus and identity? Are we developing the group culture and skills necessary to support workshop methodology? Successful models must be based on sound methodology and should foster (1) competency in mathematics, (2) a sense of academic community and intellectual focus, and (3) student cooperation. Each of these elements can help determine the success or failure of all students. We must understand the operating culture of the institution, expand the intellectual discourse to include the input of a diverse constituency, and alert students to the behaviors and practices valued in the mathematics and science classroom. Even more fundamental is our guiding philosophy concerning students of color. Our methodologies should flow from a belief that students of color can contribute

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to the culture of the university and the classroom by becoming valued contributors to the intellectual interaction within the classroom. This belief should be backed by a university commitment that views minority engineering programs as essential to the functioning of the department and academic unit. Where the minority engineering program is housed, its reporting lines, and its value to the college of engineering can significantly affect student success.

thomas g. mortenson

Financing Opportunity for Postsecondary Education Reginald Wilson has effectively identified a broad range of influences affecting opportunities for minorities in science, math, and engineering programs. I would like to more fully develop one of these areas, namely, financial aid for college students. Financial aid is by no means a guarantee of academic success in college, but I can assure you that it is a necessary condition for success. By any measure, the financial aid system in the United States today is in serious trouble, with no apparent prospects for improvement. The consequences for the distribution of opportunities for postsecondary education and training across all segments of the population are absolutely devastating to the futures of young people and the country as a whole.

Refinancing Higher Education, 1980 to . . . Opportunity for higher education costs money—a great deal of it. Capacity costs money. Quality costs money. Affordability costs money. During fifteen of the last twenty years, while changes in the labor market require ever greater levels of educational attainment, government has been cutting back sharply in the allocation of available social resources for investments in the education and training of the future labor force. This reduction has occurred at the federal level and in forty-nine of the fifty states. At the state level, where the bulk of social resources for higher education are provided, state resources have been diverted from higher education to the correctional system and Medicaid. As social resources for higher education have been reduced, institutions have sought to make up for this revenue loss by increasing charges to students. Since 1980

222 The Undergraduate Years plus One

the average annual increases in tuition and fees charged to students have averaged two to three times the annual rate of inflation. As a result, institutions—especially public institutions—are now deriving a far larger share of their operating revenues directly from students. This massive cost shift from taxpayers to students since 1979 has reduced the social contribution to investment in the future work force by about $14.2 billion as of 1992, compared to the share of total expenditures provided in 1979. About $4.5 billion of this reduction has occurred at the federal level, where loans have been increasingly substituted for grants. The remaining $7 billion has occurred at the state level, where state financial resources have been diverted from higher education to expand and operate state prisons and to finance health care for the poor through Medicaid. This means, of course, that public higher education has had to replace these lost state revenues, which is why students are being charged $14.2 billion more now for their higher education than they were fifteen years ago.

Income and Educational Attainment Economists have encouraged this shift in responsibility for financing higher education because of the extraordinary private returns on a higher education investment. Compared to the lifetime earnings of a high school graduate, a person with a baccalaureate degree can expect to earn, on average, anywhere from $465,000 to $600,000 more over the course of their working lives. The earnings gap between the high school graduate and the college graduate has been widening since the early 1970s, which has been cited by economists as justification for shifting a larger share of the financial responsibility for higher education from taxpayers to students. This earnings gap, however, is not the result of growth in the real incomes of college-educated workers. It is the result of the free fall in incomes of those with a high school education or less during this period. The number of well-paid jobs in the labor market that do not require at least some postsecondary education or training has been shrinking for decades and shows no sign of recovery. What is left for those without postsecondary education or training is increasingly minimum-wage employment.

The Affordability Problem The $14.2 billion shift in responsibility for financing higher education since 1979 has occurred without any consideration of the financial ability of students to pay these higher costs. At neither the federal nor the state level is there any apparent awareness of the consequences of this cost shift on the opportunity decisions of students. The point is that not all students come from families that are equally capable of paying either for their children’s higher education or for the huge real increases in the costs of such education that have been foisted upon them by unwilling taxpayers. Some students are dirt poor, while others are filthy rich, but most fall in between these extremes. Some students and their families can absorb much higher college

Financing Opportunity for Barriers Postsecondary to Minority Education Success

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costs without appreciably impacting their lifestyle. Many students who are already dependent on financial aid need even more to meet the higher costs that resulted from the cost shift from taxpayers to students. Some students from low-income family backgrounds need assistance with all of the attendant costs of higher education. During the last fifteen years, public policy has been oblivious to the differential impacts of the cost shift on students from different economic backgrounds. Neither the federal government nor any of the states have sought to alleviate the consequences of this cost shift for those who are dependent on financial aid to help pay their college costs. For the most part, students and their families have been left to fend for themselves. On the one hand, they are aware of the necessity of a college education to gain access to the job and lifestyle opportunities available in the United States, but, on the other hand, they are denied the means to finance their higher educations.

Consequences for Educational Opportunity and Attainment Forty years’ worth of econometric research on student demand for higher education has resulted in a simple finding that any of us could have predicted from our Economics 101 course, namely, that all things being equal, higher prices cause buyers to buy less of a service and lower prices cause buyers to purchase more of a service. The student-demand research in economics has consistently found that higher fees cause fewer students to enroll and lower fees cause more students to enroll. There are hundreds of studies that support this finding and none that refute it. Yet this important research finding has consistently been ignored for the last fifteen years in social policy design and program budgeting. The empirical consequence is clear from the enrollment data. Today higher educational opportunity is more unequally distributed across levels of family income than it has been at any time since 1970, when the Census Bureau began reporting the data from which these conclusions are drawn. Students from families whose incomes place them in the top quartile of family income (above roughly $65,000 per year) are doing well at every stage in the process of obtaining baccalaureate degrees. Students from the two middle-income quartiles (between about $20,000 and $65,000 per year) are clearly struggling and falling increasingly behind those from the top quartile in terms of educational attainment. Students from the bottom quartile of family income have been drifting off the bottom of my charts over the past fifteen years, losing out at every stage in their efforts to earn baccalaureate degrees. In 1979 a student from the top quartile of family income was about four times more likely than a student from the bottom quartile to earn a baccalaureate degree by age twentyfour. In 1993 the difference was thirteen times greater.

Social Welfare Implications Sometime during the past fifteen years we have lost sight of our common interest in extending opportunity for postsecondary education and training to a growing share of the population. At the same time that government tax revenues have become increas-

224 The Undergraduate Years plus One

ingly dependent on the higher wages earned by college-educated workers, government resources have been diverted from higher education investments into other government activities that are not socially productive. This reallocation of social resources away from profitable social investments and toward unprofitable ones has the inevitable consequence of dimming our prospects for future social welfare. The course we have entered upon promises a meaner and less secure future for our children than we could have achieved had our social resources been more productively invested.

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Appendix

Table A1 and figures A1–A18 for the appendix to “Financing Opportunity for Postsecondary Education” follow on pages 226–236.

225

table A1 Appropriations of State Funds for Operating Expenses of Higher Education perof Higher table A1 Tax Appropriations of State Tax Funds for Operating Expenses $1,000 of Personal Income $1,000 of Personal Income

State

1974–75 State 1976–77

Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

$11.54 18.42 15.80 10.24 12.01 13.64 7.40 11.18 10.91 11.29 12.74 14.78 9.45 9.32 9.65 10.47 12.58 12.54 10.89 8.13 6.54 10.44 9.71 16.12 8.59 11.33 10.51 9.44 4.95 6.73 14.40 11.13 14.93 8.71 7.09 9.17 12.08 8.17 9.99 17.06 9.98 10.05 9.44 16.08 10.70 10.31 13.15 12.53 15.08 14.67

All States

$10.36 All States $11.05

1978–79 1979–80 1980–811978–79 1981–821979–80 1982–831980–81 1983–84 1981–82 1984–85 1982–83 1974–75 1976–77

$16.03 $18.04$11.54 $16.02 $16.03 $16.29 $18.04 $14.31 Alabama 16.64$18.4216.42 19.6017.98 16.64 23.84 Alaska19.60 15.52 14.60 15.8013.41 15.5213.59 14.60 12.81 Arizona 11.76 11.81 10.2413.00 11.7612.41 11.81 11.05 Arkansas 13.10 13.47 12.0114.14 13.1013.85 13.47 12.82 California 13.60 12.66 13.6411.41 13.6010.44 12.66 10.53 Colorado 6.76 8.26 7.40 7.68 6.76 7.93 8.267.12 Connecticut 11.50 10.91 11.1810.71 11.5011.76 10.91 11.69 Delaware 9.48 10.91 9.37 9.24 9.32 9.489.05 Florida9.24 10.60 11.42 11.2911.30 10.6011.06 11.42 11.28 Georgia 17.25 16.80 12.7415.95 17.2516.20 16.80 15.83 Hawaii 16.34 14.7813.58 16.5713.74 16.34 12.47 Idaho16.57 9.34 9.45 8.76 9.00 8.77 9.348.28 Illinois9.00 10.73 10.42 9.32 9.93 10.73 9.93 10.429.81 Indiana 13.77 9.6513.10 12.7713.05 13.77 12.51 Iowa 12.77 12.73 13.39 10.4712.91 12.7311.88 13.39 11.78 Kansas 12.12 13.27 12.5812.96 12.1211.80 13.27 12.72 Kentucky 11.55 12.03 12.5412.39 11.5513.07 12.03 12.76 Louisiana 7.87 10.89 8.34 8.33 8.11 7.877.48 Maine8.33 9.68 9.34 8.13 9.34 9.68 9.50 9.348.73 Maryland 6.75 6.51 6.54 6.88 6.75 6.29 6.516.26 Massachusetts 10.51 10.55 10.4410.37 10.51 9.43 10.559.19 Michigan 14.20 13.88 9.7114.53 14.2013.28 13.88 12.96 Minnesota 16.21 18.22 16.1217.59 16.2117.41 18.22 18.08 Mississippi 9.02 8.92 8.59 8.81 9.02 8.80 8.927.97 Missouri 11.62 11.81 11.3311.42 11.6211.01 11.81 12.43 Montana 13.00 13.40 10.5112.72 13.0012.16 13.40 12.70 Nebraska 10.76 9.91 9.44 9.13 10.76 8.41 9.917.66 Nevada 5.26 4.97 4.95 4.65 5.26 4.44 4.974.67 New Hampshire 6.41 6.33 6.73 6.23 6.41 6.08 6.335.76 New Jersey 16.42 14.4015.78 14.9815.27 16.42 16.79 New 14.98 Mexico 10.52 11.1310.57 10.5210.23 10.52 10.27 New 10.52 York 16.00 North15.11 Carolina 15.91 14.9315.82 15.1115.96 15.91 15.14 8.7116.18 13.3813.99 15.14 18.97 North13.38 Dakota 7.98 7.09 7.93 8.03 7.70 7.986.82 Ohio 8.03 10.69 11.02 9.1711.13 10.6911.02 11.02 11.78 Oklahoma 13.38 13.25 12.0812.62 13.3811.09 13.25 10.27 Oregon 9.39 8.46 8.17 8.12 9.39 7.39 8.467.36 Pennsylvania 11.97 10.48 9.9910.23 11.97 9.91 10.489.50 Rhode Island 15.89 South16.05 Carolina 16.36 17.0616.31 16.0516.65 16.36 11.09 9.9810.54 11.41 9.97 11.099.64 South11.41 Dakota 9.80 11.28 10.0511.15 9.8010.51 11.28 10.05 Tennessee 11.94 9.4413.08 13.3312.46 11.94 13.99 Texas13.33 17.58 16.0816.93 17.3416.35 17.58 15.54 Utah 17.34 8.62 9.41 10.70 8.46 8.62 8.43 9.418.44 Vermont 11.00 12.08 10.3111.24 11.0011.42 12.08 10.81 Virginia 14.00 13.81 13.1514.59 14.0012.46 13.81 11.66 Washington 13.31 12.5312.88 12.9112.27 13.31 12.60 West 12.91 Virginia 13.94 13.53 15.0813.30 13.9412.76 13.53 12.06 Wisconsin 14.74 15.31 14.6714.12 14.7415.79 15.31 16.04 Wyoming

$12.64 $16.29 $11.67 $14.31 $13.73 $16.02 25.91 17.98 20.85 23.84 20.26 16.42 11.83 13.59 11.09 12.81 11.94 13.41 10.73 12.41 9.87 11.05 11.93 13.00 11.35 13.85 8.83 12.82 11.42 14.14 10.53 10.44 9.90 10.53 9.57 11.41 6.15 6.48 7.686.29 7.93 7.12 11.58 11.76 10.64 11.69 11.07 10.71 8.06 8.29 9.378.75 9.32 9.05 10.73 11.06 10.18 11.28 10.28 11.30 17.10 16.20 15.17 15.83 15.14 15.95 12.13 13.74 10.94 12.47 11.65 13.58 7.73 8.29 8.767.76 8.77 8.28 8.89 9.51 9.939.13 9.93 9.81 12.55 13.05 11.88 12.51 12.62 13.10 12.11 11.88 10.88 11.78 11.51 12.91 11.79 11.80 11.97 12.72 11.69 12.96 12.24 13.07 10.26 12.76 12.19 12.39 6.29 7.23 8.347.44 8.11 7.48 8.14 8.64 9.348.84 9.50 8.73 5.38 8.39 6.886.42 6.29 6.26 8.54 9.67 10.378.71 9.43 9.19 11.82 13.28 13.07 12.96 12.70 14.53 15.81 17.41 16.80 18.08 15.58 17.59 6.98 7.37 8.817.51 8.80 7.97 12.77 11.01 12.73 12.43 13.22 11.42 11.60 12.16 11.35 12.70 11.91 12.72 7.02 6.99 9.137.35 8.41 7.66 2.93 3.70 4.653.77 4.44 4.67 5.31 5.56 6.235.55 6.08 5.76 16.26 15.27 14.83 16.79 16.37 15.78 9.66 10.27 10.27 10.579.96 10.23 15.41 15.96 15.23 16.00 16.13 15.82 16.14 13.99 14.12 18.97 13.70 16.18 7.41 7.92 7.937.61 7.70 6.82 12.59 11.02 11.22 11.78 10.17 11.13 9.61 10.27 9.85 12.629.07 11.09 5.83 7.23 8.127.08 7.39 7.36 9.16 9.44 10.239.41 9.91 9.50 14.69 16.65 13.82 15.89 15.04 16.31 7.81 8.45 10.548.83 9.97 9.64 9.14 10.05 10.62 11.159.61 10.51 12.85 12.46 12.84 13.99 12.87 13.08 15.56 16.35 14.10 15.54 15.54 16.93 7.92 7.96 8.468.38 8.43 8.44 10.97 11.42 9.84 10.81 10.53 11.24 10.47 12.46 11.01 11.66 10.82 14.59 11.81 12.27 11.41 12.60 12.23 12.88 11.56 12.76 11.69 12.06 11.46 13.30 16.94 15.79 16.64 16.04 16.45 14.12

$12.64 25.91 11.83 10.73 11.35 10.53 6.29 11.58 8.75 10.73 17.10 12.13 7.76 9.13 12.55 12.11 11.79 12.24 7.44 8.84 6.42 8.71 11.82 15.81 7.51 12.77 11.60 7.35 3.77 5.55 16.26 9.96 15.41 16.14 7.61 12.59 9.07 7.08 9.41 14.69 8.83 9.61 12.85 15.56 8.38 10.97 10.47 11.81 11.56 16.94

$11.22$10.36 $11.16 $11.05 $10.88 $11.22 $10.64 $11.16 $10.12 $10.88 $10.45 $10.64 $10.28

$10.12

(Source: Postsecondary Education OPPORTUNITY, 1994) (Source: PostsecondaryOctober Education OPPORTUNITY, October 1994)

226

on per Change: 1978–79 to 1994–95

984–85 1985–86

1987–88

1988–89

1989–90 1990–91

$13.73 20.26 11.94 11.93 11.42 9.57 6.48 11.07 8.29 10.28 15.14 11.65 8.29 9.51 12.62 11.51 11.69 12.19 7.23 8.64 8.39 9.67 12.70 15.58 7.37 13.22 11.91 6.99 3.70 5.56 16.37 10.27 16.13 13.70 7.92 10.17 9.85 7.23 9.44 15.04 8.45 10.62 12.87 15.54 7.96 10.53 10.82 12.23 11.46 16.45

$15.69 26.98 11.96 12.99 11.34 9.23 6.32 10.90 8.07 9.86 15.40 12.06 8.27 9.43 11.25 10.89 11.29 11.87 8.07 8.47 8.30 10.02 13.11 17.49 7.46 12.45 10.77 7.78 3.90 7.31 16.06 10.02 16.13 14.68 8.17 11.08 10.05 7.26 8.95 15.13 7.93 11.15 10.97 15.52 7.80 10.27 10.59 12.27 11.02 17.65

$12.42 16.55 11.00 10.63 10.41 8.86 6.55 10.67 7.99 9.25 16.10 12.37 7.40 9.75 11.60 10.10 11.92 10.21 9.37 8.17 8.66 9.72 12.82 14.20 7.20 10.87 10.35 7.58 4.09 7.14 14.37 9.66 16.30 13.95 8.41 9.52 9.73 6.94 8.88 13.66 8.86 11.09 9.92 14.07 7.00 10.27 10.13 11.66 10.60 17.61

$15.65 17.21 11.23 10.42 10.17 9.25 6.84 10.00 7.87 9.13 16.15 12.24 7.35 9.82 11.87 10.21 11.53 9.44 9.81 8.46 7.75 9.45 12.74 15.76 7.35 10.54 11.10 7.36 3.91 7.23 15.09 9.69 15.57 13.51 8.38 10.11 9.44 6.98 9.05 14.02 8.70 10.78 9.65 13.60 6.87 10.59 10.16 12.08 10.42 18.43

$14.73 17.59 10.91 10.29 10.81 9.29 6.22 9.91 7.66 9.14 15.90 12.46 8.21 9.82 12.09 11.31 11.51 9.65 9.71 9.14 6.66 9.21 13.19 14.87 7.60 10.57 12.27 7.94 3.53 6.73 15.75 9.21 15.71 16.34 8.46 10.49 9.61 6.99 8.62 13.66 9.46 10.71 10.68 13.21 7.03 10.42 10.32 11.42 10.55 17.81

$10.28

$10.22

$9.68

$9.60

$9.74

1991–92

1992–93

1993–94

1994–95

Dollars

Percent

$15.45 15.94 10.92 10.28 10.53 8.88 6.08 9.84 7.30 9.30 14.49 13.24 7.85 9.93 13.12 11.07 11.86 10.35 9.86 8.97 5.32 9.19 13.38 14.43 7.58 10.28 13.23 7.63 3.25 5.74 16.71 8.31 14.86 14.49 8.51 11.16 9.36 6.84 7.88 13.47 9.34 10.25 9.67 13.25 6.45 9.34 10.00 11.46 10.54 17.54

$13.02 15.05 10.31 11.49 9.14 8.40 6.01 9.03 6.15 7.88 14.17 12.70 7.42 9.59 11.77 9.91 11.59 9.38 8.83 7.69 4.30 9.02 12.11 12.49 6.36 10.81 12.26 8.23 3.25 5.87 16.12 6.94 13.34 15.03 7.66 11.15 9.37 6.67 6.15 11.97 8.85 8.93 9.90 13.21 5.64 8.43 9.74 11.29 10.02 16.93

$12.98 14.49 9.74 11.87 7.64 8.10 5.68 8.65 5.61 8.24 14.21 12.09 7.18 9.28 12.45 10.19 10.72 9.70 8.03 7.31 4.63 8.81 11.39 12.66 6.39 9.93 12.71 8.17 3.08 5.91 16.10 6.63 13.58 14.70 7.08 11.30 9.45 6.01 6.16 11.50 9.24 9.16 9.37 13.36 5.38 7.40 9.31 11.05 10.16 15.69

$13.08 13.67 9.29 10.93 6.57 7.46 5.56 8.23 5.99 8.29 14.70 11.42 7.07 8.81 11.94 9.78 9.97 8.34 7.71 6.58 5.79 8.33 11.02 12.43 6.17 8.73 11.77 6.87 3.20 5.93 15.98 6.82 13.25 13.14 7.08 10.19 7.81 6.18 5.62 10.18 9.22 9.06 9.85 12.84 5.03 7.03 8.81 10.69 9.76 14.74

$14.19 12.44 9.33 10.80 6.95 7.10 5.46 9.03 5.99 8.43 14.09 11.77 7.19 8.44 12.44 9.99 10.24 8.27 7.44 6.64 6.14 8.26 10.86 16.17 6.57 7.74 11.64 6.15 3.42 5.98 16.57 6.89 13.28 13.23 7.16 9.83 7.37 6.18 5.90 10.36 8.78 9.21 9.01 13.25 4.75 6.99 8.24 10.34 9.81 13.87

$–3.85 $–4.20 $–5.27 $–1.01 $–6.52 $–5.56 $–2.80 $–1.88 $–3.49 $–2.99 $–2.71 $–4.57 $–2.15 $–1.98 $-1.33 $–3.40 $–3.03 $–3.76 $–0.43 $–2.70 $–0.37 $–2.29 $–3.02 $–2.05 $–2.35 $–4.07 $–1.76 $–3.76 $–1.55 $–0.35 $0.15 $–3.63 $–2.63 $–1.91 $–0.82 $–1.19 $–5.88 $–2.28 $–4.58 $–6.00 $–2.31 $–2.07 $–2.93 $–4.33 $–4.66 $–5.09 $–5.57 $–2.97 $–3.72 $–1.44

–21.3% –25.2% –36.1% –8.6% –48.4% –43.9% –33.9% –17.2% –36.8% 26.2% 016.1% –28.0% –23.0% –19.0% –9.7% –25.4% –22.8% –31.3% –5.5% –28.9% –5.7% –21.7% –21.8% –11.3% –26.3% –34.5% –13.1% –37.9% –31.2% –5.5% 0.9% –34.5% –16.5% –12.6% –10.3% –10.8% –44.4% –27.0% –43.7% –36.7% –20.8% –18.4% –24.5% 24.6% –49.5% –42.1% –40.3 –22.3% –27.5% –9.4%

$9.39

$8.62

$8.21

$7.96

$7.99

$–3.23

–28.8%

227

figure A1 Higher Education Revenues by Source, 1993. Data source: National Income and Product Accounts

figure A2 Higher Education’s Share of the Gross Domestic Product, 1952–1993. Data source: National Income and Product Accounts

228

figure A3 Higher Education’s Share of Expenditures of State and Local Governments, 1952– 1993. Data source: National Income and Product Accounts

figure A4 Higher Education’s Share of Expenditures of the Federal Government, 1952–1993. Data source: National Income and Product Accounts

229

figure A5 Higher Education’s Share of Personal Consumption Expenditures, 1952–1993. Data source: National Income and Product Accounts

figure A6 Distribution of Responsibilities for Financing Higher Education, 1952–1993. Data source: National Income and Product Accounts

230

figure A7 Median Family Income by Educational Attainment of Householder, 1992. Data source: Census Bureau

figure A8 Median Family Income by Educational Attainment of Householder, 1956–1992. Data source: Census Bureau

231

figure A9 Change in Median Family Income by Education Attainment of Householder, 1973– 1992. Data source: Census Bureau

figure A10 High School Graduation Rates by Family Income Quartiles for Unmarried 18–24 Year Olds, 1970–1993. Data source: Calculated from Census Bureau Data

232

figure A11 College Participation Rates by Family Income Quartiles for Unmarried 18–24 Year Old High School Graduates, 1970–1993. Data source: Calculated from Census Bureau Data

figure A12 Estimated Four-Year College Completion Rates by Age 24 by Family Income Quartiles for Unmarried College Students, 1970– 1993. Data source: Calculated from Census Bureau and High School and Beyond Data

233

figure A13 Estimated Chances for a Baccalaureate Degree by Age 24 by Family Income Quartile, 1970–1993. Data source: Calculated from Census Bureau and High School and Beyond Data

figure A14 Distribution of Public University Freshmen and Their Financial Need by Family Income Levels, 1993–1994. Data source: College Board Cost Data, Federal Methodology Need Data, and UCLA Freshman Survey Family Income Data

234

figure A15 Distribution of Public Four-Year College Freshmen and Their Financial Need by Family Income Levels, 1993–1994. Data source: College Board Cost Data, Federal Methodology Need Data, and UCLA Freshman Survey Family Income Data

figure A16 Distribution of Public Two-Year College Freshmen and Their Financial Need by Family Income Levels, 1993–1994. Data source: College Board Cost Data, Federal Methodology Need Data, and UCLA Freshman Survey Family Income Data

235

figure A17 Distribution of Private University Freshmen and Their Financial Need by Family Income Levels, 1993–1994. Data source: College Board Cost Data, Federal Methodology Need Data, and UCLA Freshman Survey Family Income Data

figure A18 Distribution of Private Four-Year College Freshmen and Their Financial Need by Family Income Levels, 1993–1994. Data source: College Board Cost Data, Federal Methodology Need Data, and UCLA Freshman Survey Family Income Data

236

vi

EARLY CAREER PREPARATION

Academia

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shirley vining brown

The Preparation of Minorities for Academic Careers in Science and Engineering How Well Are We Doing?

s we approach the end of the final decade of this century and enter the next, di-

A versifying America’s faculty is part of a larger discussion about how to meet the

needs of institutions and a society with expanded missions. One mission in higher education is to increase the number of non-Asian minority and women faculty in scientific careers. In order to assess the status and advancement of minorities in the science and engineering (S/E) academy, this chapter will address two related educational policy questions: Why are there so few minority faculty in S/E departments? What can be done to alleviate minority shortages on S/E faculties? Scanning current research, we find only partial answers to these questions. Thus, the thesis of this chapter is twofold. First, the position of minorities in the S/E academic work force is complex and the problems they encounter call for different solutions. Second, although the answers are not simple, they do exist. By reviewing the literature, what is known and still remains to be discovered will inform new questions and policy directions raised later in the discussion. This chapter is divided into four sections. Following this brief introduction, the first section reviews the representation of S/E minority faculty in academe, pointing out their past and current status in the academic work force. The second section examines approximately where in the education pipeline potential minority faculty are lost, how these strategic losses are related to the sparse number of minorities in graduate S/E education, and what research tells us about why minorities leave S/E degree majors. The third section discusses what we know about the barriers that deter minority students from pursuing S/E careers. Much of this research is qualitative and describes at first hand information from minority students about administrative policies, practices, and students’ experiences in S/E education. Although minority women are considered throughout, this section describes how race/ethnic status and gender converge to influence the perceptions and experiences of minority women in gradu239

240 Early Career Preparation: Academia

ate S/E education. The final section returns to the issue of minority faculty shortages, raising new questions and recommendations to guide future science education research, policies, and practices.

The Status of Minority Scientists and Engineers in Academe The instructional faculties in American S/E graduate education number approximately 175,000 individuals and in 1991 served approximately 2.4 million S/E undergraduate and graduate students (National Center for Education Statistics 1993). However, only 5 percent1 of all S/E doctorates awarded in 1994 went to American Indian, black, and Hispanic citizens. While disagreements continue concerning the reasons behind minority faculty shortages and policy differences and how best to solve them, there is no dispute over minority shortages, particularly in S/E career fields. Minority S/E Faculty Representation Past and Present The growing body of research on minorities in S/E is generally restricted to the following themes: the general status of minorities in S/E careers (Jay 1971, NSF 1994, Taylor 1955, White 1993); the status of minority students in S/E (Berryman 1983; Brown 1989, 1990, 1995; Clewell and Anderson 1992; Green 1946; Hill 1990; Jay 1971; Mestre 1986; Pearson 1985; Rodriguez 1984, 1993; Pearson and Bechtel 1989; Pearson and Fechter 1995); discrimination against minorities in science (Brown 1995b, Cole and Cole 1973, Zuckerman 1971); the status of minority women in the science work force (Adler 1994; Brown 1994; Koelewijn-Strattner 1990; KoelewijnStrattner, Adler, and Lengermann 1991; Leggon 1987; Malcom, Hall, and Brown 1975; Tolbert 1993); and minority attrition from S/E majors (Brown 1993; Campbell 1992; Hewitt and Seymour 1991, 1994; NSF 1994). Moreover, numerous dissertations have been written on minority students in S/E degree majors. Little is known, however, about minority faculty in S/E departments or how the academic marketplace works for these groups. Green’s (1946) study of black doctorates revealed that in the 1940s 96 percent (N = 345) were employed in educational institutions, including colleges, universities, and secondary schools. Most were college professors (N = 267), although several presidents (N = 22) and deans (N = 19) were among this group. So few blacks received doctorates in the 1940s that they were overlooked in national assessments of American scientists. As Taylor (1955) has noted, “Black institutions were subject to special social situations which would prevent fair comparison with other institutions in a homogeneous sample.” Even well-trained black scientists such as Edward Bouchet, a Phi Beta Kappa at Yale, went unrecognized for their contributions to science. Nor were black scientists able to find suitable appointments in mainstream academic institutions. George Washington Carver in chemistry, Elmer Imes in physics, and Ernest E. Just (another Phi Beta Kappa) in marine biology were among the few black academic scientists who achieved national recognition in our century (Taylor 1955). Nonetheless, these scientists were restricted to academic employment in traditionally black institutions (TBIs). The scant number holding faculty positions in traditionally

The Preparation of Minorities for Academic Careers in Science and Engineering 241

white institutions (TWIs) were at the University of Chicago (N = 2), the City College of the City University of New York (N = 2), and Wayne State University (N = 1). Silver, Dennis, and Spikes (1988) found that the infusion of money during the 1970s had no real effect on increasing minority S/E faculty in the 1980s. Of 474 black faculty surveyed in selected Adams states (identified in Adams v. Richardson, 1973. Washington, D.C., Circuit Court. Federal Register), only 25 were identified as teaching faculty in mathematics, computer science, engineering, biology, physics, and chemistry. Although faculty responses were not identified by career field, Silver and his colleagues found that black and white faculty clearly differed in their responses to certain questions. For example, black faculty: acquired academic positions through non-networking pathways such as newsletters, ads, and journals; spent considerable time on minority-affairs activities; had publication track records comparable to white faculty but received pay increases that lagged behind their white colleagues; and were less often tenured even if more experienced or of higher rank than their white colleagues. Other studies have found additional ways that minorities are limited by academic appointments.

Limited Employment Opportunities Except for engineering, both absolute and proportional reductions exist in the number of minority doctorates planning to enter the academic work force (Brown 1988; NSF 1994). Nor are minorities considered for the full range of available opportunities in the academy. Garza (1988) has discussed this issue in terms of the “barriorization” of Hispanic faculty. He found that the latter were not offered the range of faculty positions available to white faculty, being generally restricted to ethnic studies and Spanish departments. Minority faculty in S/E departments are similarly limited by the quality of the institutions in which they work, the type of work they do, and the career specialties they enter. Quality of institutions. Minority faculty are employed in very different institutional environments than their white counterparts. A case in point are black S/E faculty employed in institutions in the Adams states who work in qualitatively different academic environments than their white counterparts (Jackson 1988). Using a composite measure of institutional quality,2 Jackson found a significant number of black scientists concentrated in mathematics, biological science, and physical science departments classified as low-to-medium in quality, reflecting the stratified nature of the academic marketplace in the 1970s. Blacks working in higher quality departments were employed in engineering and the physical sciences, primarily in institutions referred to as “traditionally black affiliations” that were presumably TBIs. Black women held no faculty positions in engineering, being primarily employed in lowquality computer and information sciences and biological science departments. Jackson contends that the educational resources available in institutions where blacks were employed limited their career advancement and the quality of instruction they were able to provide to students.

242 Early Career Preparation: Academia

Restriction-in-range of primary work activity. Although the primary work activities of S/E doctorates employed in higher education differ according to career field (Brown 1988), minority academics are usually hired as teaching faculty. Just over a third, or 34 percent, of black doctoral scientists and engineers are teaching professionals— down 10 percent from their 1985 level—followed by 29 percent for American Indians and 25 percent for Hispanics (NSF 1991). Among black academics, men and women are equally represented in teaching positions. By contrast, more Hispanic women than men are employed as teachers, whereas more American Indian men than women are employed in teaching positions. Few minorities serve as academic administrators in S/E departments; of those who do, most serve as administrators of minority or student-related services. Unlike Asian Americans, who are generally engaged in research, few black and Hispanic academic scientists are employed as research scientists. A larger percentage of white academics are employed in four-year institutions (45%), where about half are employed in teaching positions (23%), whereas white and black scientists are about equally represented in administration (16% and 19%, respectively). Similar to Asian-Americans, however, more white scientists are engaged in research and development positions (35%). Career field differences. Minority academic careers are related to the narrow choice of disciplines they are offered. If one considers where minority faculty are concentrated, annual statistics show that most are employed in the social and behavioral sciences—ranging from 35 percent for American Indians and Hispanics to 55 percent for black doctorates (NSF 1991). When considering postdoctoral plans, engineering is the only career field showing an increase in minority Ph.D.s planning to enter academe (Brown 1988). The increase is based on extremely small numbers, however. For example, although engineering is one of the more active growth fields in the academic market, in 1975 only 7 minorities (1 Asian American, 3 blacks, and 3 Hispanics) out of 320 Ph.D.s had confirmed employment plans in academic engineering. By 1990 the number of Asian Americans and Hispanics rose to 9 and 10 Ph.D.s, respectively, while the number of black engineers planning academic careers increased from 3 to 4. As will be mentioned later in this chapter, foreign-born engineering doctorates are being hired at a faster rate than their U.S. counterparts. Two- and four-year institutions. Currently over half of all black (56%), American Indian (52%), and Hispanic (54%) doctoral scientists and engineers in higher education are employed in four-year institutions. However, as White (1993) has shown, black faculty comprised roughly 2 percent of scientists and engineers in four-year institutions, while American Indians represented less than 1 percent. National Science Foundation (1991) data show the percentage of black women employed in twoyear educational institutions was double that of black men (6.6% vs. 3.3%), reflecting a slightly higher proportion of black women than men employed in educational institutions (59.4% vs. 55.1%). Whether they are in two- or four-year institutions, few black faculty teach in physical and life science departments. Among Hispanics, women and men are about equally distributed in S/E education (58% vs. 53%) but, like blacks, more Hispanic women than men are employed in two-year institutions (6.5% vs. 2.2%). Unlike blacks, however, Hispanics teaching

The Preparation of Minorities for Academic Careers in Science and Engineering 243

in two-year institutions are overrepresented in the physical sciences and underrepresented in social science departments (Brown 1988). A roughly equal percentage of American Indian women and men are represented in four-year institutions (51%). Although Asian Americans are least likely to be employed as S/E faculty (38%), of those who are, more Asian American women than men are faculty members (46% vs. 37%), particularly in two-year institutions (3.6% vs. .09%). Factors Related to Minority Faculty Status If we assume that the possession of a Ph.D. levels the differences between groups and is a prerequisite for academic employment, no measurable differences should exist between comparable S/E faculty in career advancement, remuneration, or primary work activities. Yet it has been argued that the long-term investment in an earned doctorate does not always pay off for minorities seeking traditional faculty and research careers (Brown 1988; Nettles 1987). Pressing the point, Cole and Cole (1973) maintain that the lack of data on the ability or scientific output of black (and presumably other) minority scientists makes it difficult to say with certainty whether the positions they occupy are the result of a self-fulfilling prophecy or racial discrimination. Instead, they believe most inequities occur while blacks are still in training rather than when they enter the professional world of science. They believe that the universal criteria used by organizations to hire, promote, and advance faculty provide important safeguards against particularism in these processes. But does the academic marketplace work the same way for minorities as it does for white and Asian American scholars in faculty rank, primary work assignments, and salary? Are there gender differences among minority faculty? Academic rank. Promotion to senior ranks (e.g., associate and full professor) is the goal of all faculty with academic career aspirations. During the 1970s, better than half of black faculty were in the lowest employment ranks (Moore and Wagstaff 1974; Raftky 1972). This changed during the 1980s, when black faculty holding associate professorships increased from 12 to 16 percent (Jackson 1988). One must keep in mind, however, that these studies are based on aggregate data rather than field-specific data and provide little firsthand information about the status or change in the racial composition of S/E faculty. A clearer picture of how minority faculty fare in specific academic career fields can be seen in national statistics. For instance, black doctoral faculty can be found in the two lower ranks in the life science and social science departments but are clustered in the associate- and full-professor ranks in the physical sciences and engineering (Brown 1988). This means that few black assistant professors are being hired in engineering and the physical sciences, a position that generally serves as a pipeline to tenured faculty status. It is not surprising that black faculty are concentrated in the senior ranks in these fields—particularly at the associate rank—because for over a decade the United States has experienced a shortage of engineering faculty. In trying to fill faculty positions, one source of new hires was experienced engineering doctorates working outside academe rather than new doctorates (Syverson and Forster 1984). Another source of new faculty entrants came from foreign-born Ph.D.s. With

244 Early Career Preparation: Academia

sudden drops in the number of U.S. citizens earning doctorates in engineering and the sizable number of foreign doctorates remaining in the United States, engineering has come to rely on foreign talent to fill entry-level teaching positions. Thus, almost 75 percent of new engineering assistant professors are foreign-born (Drucker 1988). For minorities this means that if entry-level positions are being filled by foreigners instead of U.S. citizens, most black and other minority faculty with engineering doctorates will be located at the associate- or full-professor ranks. The academic ranking among other minorities differs from that of black doctorates. Hispanic faculty with doctorates are concentrated at the associate- and fullprofessor ranks in the physical and life science departments, while the distributions of Asian Americans are irregular and differ from the distributions of other minorities in almost every department. Compared to Hispanics and blacks, there are fewer Asian Americans in the top two ranks (particularly at the associate-professor level) in physical science departments, where the faculty is aging (Porter and Czuijko 1986). Most Asian Americans in senior faculty ranks are in the life sciences, social sciences, and engineering departments, where they are concentrated at the full-professor rank. Employment Status Full-time faculty. Brown (1988) found that few black doctorates are appointed to full-time positions in engineering departments in four-year institutions and no appointments are made or expected in engineering departments in two-year institutions. Figure 1 shows that minority faculty are still underrepresented in full-time teaching positions in S/E degree fields such as geology, mechanical engineering, and civil engineering, where blacks represent 1 percent of the faculty and Hispanics 2–3 percent of full-time faculties. Too few American Indians were teaching in any S/E field to calculate a percentage. Part-time faculty. Minorities are generally overrepresented among part-time or adjunct faculty in higher education (Digest of Education Statistics 1993; Jackson 1988), but in S/E career fields minority part-time faculty are no better off than their full-time counterparts (see figure 1). Black scientists and engineers represent only 1 percent of the part-time faculty in geology, 2 percent in physics, and 3 percent in electrical, mechanical, and civil engineering, respectively. Hispanic faculty represent 1 percent of part-time faculty in geology and physics and 2 percent in electrical, mechanical, and civil engineering, respectively. Thus, whether full or part time, underrepresented minority faculty are almost without representation on the faculties in S/E departments. Salaries. Salary differences exist between minorities and whites in both the academic and nonacademic marketplace, although the pay scale is higher in the nonacademic S/E employment sector. Some salary differences are field-related. For example, black S/E faculty are more likely to hold doctorates in the social and behavioral sciences, where salaries are lower (Brown 1988; Maxfield 1981). But even in career fields where faculty salaries are higher, a continuing trend shows U.S. faculty, on average, earning higher salaries than black and Hispanic faculty (White 1993). For

The Preparation of Minorities for Academic Careers in Science and Engineering 245

figure 1 Full-time and Part-time Teaching Faculty in Selected Science and Engineering Degree Fields by Race/Ethnicity: U.S. Citizens 1991, 1992. Data source: National Science Foundation

example, the earnings gap between white and minority S/E academics ranges from almost $5,000 more earning by whites than American Indians to $6,000 more earning by whites than blacks and Hispanics. Among S/E teaching faculty, the differences are even greater: white faculty salaries are $8,400 higher than black faculty salaries and $6,200 greater than Hispanic faculty salaries. One unusual finding shows the median salary of American Indian faculty is about $200 more annually than the median salary of white faculty ($55,000 vs. $54,800). None of these differences, however, consider variations in rank or years of service, which could produce large salary differences between white and minority faculty. Regardless of race or ethnicity, salary differences have always distinguished the earnings of men and women. NSF (1991) data show that black men earn substantially higher salaries than black women in four-year institutions ($51,700 vs. $48,400), particularly among teaching faculty, where the salaries of men are $2,500 higher than women ($47,200 vs. $44,700). The same pattern exists among Hispanics, where men earn higher salaries than women in four-year institutions ($53,300 vs. $42,400), twoyear institutions ($52,700 vs. $41,900), and among teaching faculty ($52,300 vs. $43,400). Sex differences are not available for American Indians because too few women reported annual earnings. Consistent salary differences between women and men may reflect differences in employment field, length of service, rank, and job position. Even so, there are consistent gender differences within certain salary categories. For example, black men with S/E doctorates earn higher salaries than black women in the biological sciences; the same holds true for Ph.D.s with five to nineteen years of work experience, as well as those employed in business/industry and government, engaged

246 Early Career Preparation: Academia

in research, or employed in positions of management and administration. By contrast, black women earn higher salaries than black men in the social sciences and among scientists and engineers with twenty years or more of professional work experience. Similar differences show higher salaries for Hispanic men than Hispanic women, with one exception: Hispanic women with ten to fourteen years of professional work experience earn higher salaries than Hispanic men. Though consistent, these discrepancies indicate the need for more systematic analyses to sort out whether differences between minority women and men are associated with gender discrimination or cumulative factors that have little or nothing to do with gender. Faculty Perceptions in Academe We know almost nothing about the experiences of minority faculty in S/E departments. Nonetheless, the attitudes and perceptions of minority faculty in other career fields may be relevant to the experiences of minority S/E faculty in departments where they are isolated. A study by the American Chemical Society (1992) on job discrimination, career opportunities, and the status of chemists revealed almost no differences in complaints about racial and ethnic harassment by employment sector (academic vs. nonacademic), age, or sex. Proportionally more black chemists (59%), however, reported occurrences of racial harrassment on the job than Hispanic (19%), Asian (14%), or white (2%) chemists. Silver, Dennis, and Spikes (1988) found that almost 40 percent of the black faculty in the TWIs in their study said that their professional ambitions were not achievable, nor were working relationships easily developed with colleagues in their departments. Similar to Jackson (1988), Silver and his colleagues found that minority faculty were dissatisfied with the status of affirmative action and low minority faculty representation on their campuses. However, the nature of minority faculty dissatisfaction with affirmative action is at odds with the views of the majority on racial and ethnic diversity. Take, for example, the Carnegie Foundation’s (1989) nationwide survey, which found that almost three-fourths (71%) of all U.S. faculty members were satisfied with or had no opinion about the pace of affirmative action on their campuses. Engineering departments had the highest rate of faculty satisfaction or indifference to affirmative action (81%), even though nationally engineering has the lowest percentage of underrepresented minorities on its faculty. This attitude has grown and is currently flourishing; California and Texas are in the process of dismantling their affirmative-action statutes, and seventeen other states are considering legislation to do the same. Summary Although the sparce literature on minority S/E faculty raises more questions than it answers, it does reveal several trends in the S/E academy: • Minorities currently represent less than 3 percent of full- or part-time faculty employed in S/E career fields. • Most minorities are employed in the social and behavioral sciences.

The Preparation of Minorities for Academic Careers in Science and Engineering 247

• Engineering is the only career field showing an increase of minority faculty in four-year institutions. However, few minority faculty are in engineering—a field that could employ more junior minority faculty if there was less reliance on foreign-born talent to fill entry-level positions. • Minority faculty have not achieved parity with white faculty in academic rank, range of work activity, or earning power. Moreover, the quality of departments where black faculty are employed has generally been in the low-to-medium range. • Some minority faculty believe they are unable to reach their professional goals in TWIs or develop satisfactory work relationships with white colleagues. Minority faculty are dissatisfied with the pace of affirmative action, which may be more pronounced in S/E career fields, where minorities are underrepresented and isolated. • A gender gap exists among minority S/E faculty, showing women to have lower participation rates and salaries and higher representation in twoyear and less prestigious institutions.

Minority Pipeline and Training Issues: The Transition from Undergraduate to Graduate School Research on Field Switching: Where Are the Strategic Points of Loss? Education is central to the economic progress of Americans, particularly minority citizens, who have not achieved occupational or earning parity with white Americans. Two important reasons may explain why the flow of minorities into the fields of science and technology is slow. First, minority students lack exposure to science and, second, they have no minority role models to attract and guide them into these careers (Brown 1994, Clewell and Anderson 1992, Thomas, Clewell, and Pearson 1992). Moreover, the sorting influences of advanced mathematics courses in high school (Betz 1990) and the field switching that occurs before, during, and after undergraduate school further depletes the minority talent pool. In short, a major deterrent to minority advancement is the small size of the talent pool that feeds the S/E graduate programs producing potential minority faculty. Higher education may provide answers to questions about minority faculty shortages if one realizes that the standards and opportunities for future access are controlled by institutional gatekeepers who choose students much like themselves “to carry on their legacy” (Lorber 1984). Even Cole and Cole (1973) place the blame for minority S/E personnel shortages squarely with the American educational system. According to these researchers, lopsided race and gender statistics in the work force are directly linked to earlier discriminatory practices in educational institutions where students are trained. They believe that as individuals advance from one level of the educational system to the next, and finally into the work force, discrimination diminishes because advancement is based on evaluations defined by “clearer and more rational” criteria. If this assumption is correct, the disparities that occur during the formative years of S/E training should taper off at the higher levels of education

248 Early Career Preparation: Academia

(Clewell and Anderson 1992). However, national statistics (NCES 1993) have identified talent-flow gaps that challenge this assumption, claiming strategic losses as students advance in the educational system. Attrition During the Undergraduate Years Losing the war of attrition has academicians and minorities asking the same questions: Where are the strategic points of loss? Why do minorities leave S/E degree majors? The sparse research on field switching provides partial answers to these questions. Although minority enrollments are rising in most degree fields, attrition is chipping away at their gains in S/E education. There are two postsecondary points in the educational system where minority-student losses are decisive: after high school (see table 1) and after the baccalaureate degree. College Career Aspirations Before examining minority field-switching patterns, one should ask this question: Are minorities interested in careers in science and technology? The answer is “yes” if one considers the career aspirations of high school seniors as they head for college. One of the ironies of higher education is that minority students generally have higher educational aspirations than white students. Baker (1994) found that the percentage of black high school seniors with S/E career plans exceeded that of white high school seniors in all ability groups (quartiles 1–4), and the findings were similar for Hispanics. But Baker’s study also shows that six years out of high school, the overall attrition of blacks and Hispanics (82.9% and 84.9%, resp.) from scientific fields was higher than the attrition rates of white and Asian students (72.2% and 65%, resp.). More important, minority attrition was highest for blacks and Hispanics among the most able students (quartile 4), which suggests that attrition occurs for reasons other than

table 1 Probable S/E Majora in College and Earned S/E Degrees, by Education Level, Year, Race/Ethnic Group,b and Sex (%)

Year and degree High school diploma (1983) B.S. degree (1989–90) M.A. degree (1989–90)

American Indian women

American Indian Black men women

Black men

Hispanic women

Hispanic men

White women

White men

13

29

18

30

26

28

14

35

7

20

10

17

8

22

8

23

3

12

3

11

4

17

5

20

Sources: Cooperative Institutional Research Program, UCLA, American Freshman Norm Survey, unpublished tabulations, 1983, 1985, 1990; National Science Foundation, Doctorate Records File, 1988–1990; U.S. Dept. of Education, National Center for Education Statistics, Integrated Postsecondary Education Data Systems, 1988–1990. aS/E

majors include engineering, physical sciences, life sciences, environmental sciences, computer sciences, and mathematics. may be of any race.

bHispanics

The Preparation of Minorities for Academic Careers in Science and Engineering 249

ability. Another of Baker’s findings consequential for admissions policies is that black students in the three lowest ability groups completed S/E degrees at a higher rate than comparable white students, leaving Baker to conclude that “test scores measure achievement differently for Caucasians” (6). Table 1 shows that although fewer minority men than white men in high school expressed an interest in S/E careers, the proportions are similar. The aspirations of minority and white high school women are comparable, whereas the aspirations of Hispanic women are substantially higher. These data also reveal that, regardless of race, significantly fewer women (except Hispanics) than men plan to pursue S/E degrees. Thus, because interest in S/E careers wanes considerably after high school (Brown 1994, Hewitt and Seymour 1991), it is important to nurture and maintain the interest of women during this transition period. The general decline in S/E careers can be seen in the differences between career plans and degree attainment (see table 1). The interest gap widens considerably between college entry and the master’s degree (except for Hispanics, who have a rate of attainment comparable to white S/E majors). There is a sharp drop in the percentages of women and American Indian and black men who subsequently earned S/E master’s degrees. Thus, contrary to Cole and Cole’s (1973) assumption, the aspirations, access, and retention of some minorities (and women) in science and engineering not only diminishes with time but initial disparities “fan out” (i.e., grow wider) as they advance in the educational system. Field Switching Minority attrition can be caused by dropping and stopping out of school as well as by students deciding to switch degree fields. Because institutions fail to keep data on students who drop out (Thomas, Clewell, and Pearson 1992), reliable statistics are rare on the dropout and stop-out rates of minority S/E majors. However, information does exist on field switching. Why do minority students leave S/E for other degree majors? While it is difficult to determine whether field switching is more related to self- or social-selection processes (e.g., voluntary vs. involuntary), research indicates that large numbers of minorities leave the S/E pool following the baccalaureate degree (Brown 1993; Hewitt and Seymour 1991; OTA 1988). This means that in addition to attracting minorities to the sciences, a harder job may be keeping minority students on the science pathway once they enter the higher education pipeline. Field Switching: The Transition from Undergraduate to Graduate School The attrition that occurs after the undergraduate degree siphons off a smaller but sizable number of potential minority S/E faculty. Although the retention rates of minority and white engineering Ph.D.s are comparable, minority losses in the life and physical sciences are remarkable, particularly among black students, who lose 50 percent or more students to other doctoral programs (Brown 1993). Losses in the physical sciences largely result from the erosion of minority women (see table 2). Almost three-fourths of the black women (73% vs. 52% of black men),

250 Early Career Preparation: Academia table 2 Field-Switching Patterns of Minority Women in the Life Sciences and Physical Sciences, 1975–1990 Degree fields

Switchers (%)

Non-switchers (%)

Total (N)

38 56 43 36 29 33

62 44 57 64 71 67

89 1,030 97 101 196 24,824

57 73 47 30 44 47

44 27 53 70 56 53

115 823 197 173 334 11,638

Life sciences American Indian Black Mexican American Puerto Rican Other Hispanics All women Physical sciences American Indian Black Mexican American Puerto Rican Other Hispanics All women

Source: National Science Foundation, Doctorate Records File, 1975–1990.

over half of the American Indian women (57% vs. 36% of American Indian men), and almost half of the Mexican American women (48% vs. 34% of Mexican American men) and Other Hispanic women (45% vs. 28% of Other Hispanic men) left the physical sciences. Losses in the life sciences are slightly lower, but black women (56% vs. 49% of black men) and Mexican American women (43% vs. 26% of Mexican American men) still have higher than average attrition rates. Field Switching: Traditionally Black Versus Traditionally White Institutions The higher rate of black attrition in the life and physical sciences may be linked to undergraduate sources. Brown (1994) found that although recruitment of blacks from TBIs is a common practice among some S/E graduate programs, national data show that attendance at TBIs is related to field switching in certain degree majors. Table 3 reveals that where a student earns the baccalaureate makes almost no difference in the attrition rates of blacks in engineering and the life sciences. But in the physical sciences knowing where black students earned the baccalaureate degree makes a significant difference in undergraduate-to-graduate losses (χ2 = 50.7; p = .000). Why are black students from TBIs more at risk for leaving the physical sciences than their counterparts from TWIs? An examination of field switching by gender reveals one reason for this occurrence (see table 4). Although a significantly (p = .000) large percentage of black women and black men (81% and 62%, resp.) switched from the physical sciences, the relative odds of switching majors is 2.67 times greater for black women than for men. Whereas the proportions of black switchers graduating from TWIs are lower than for blacks at TBIs, being a woman

The Preparation of Minorities for Academic Careers in Science and Engineering 251 table 3 Field-Switching Patterns of Black Ph.D.s Who Earned S/E Baccalaureate Degrees at TBIs and TWIs, 1975–1990 (%) Baccalaureate degree and institution

Switchers

Non-switchers

Life sciences TBIa TWIb

51 55 χ2 = ns

49 45

29 27 χ2 = ns

71 73

68 48 χ2 = 50.72 df = 1 p = .000

32 52

Engineering TBI TWI

Physical sciences TBI TWI

Source: National Science Foundation, Doctorate Records File, 1975–1990. aTraditionally bTraditionally

black institutions. white institutions.

at both types of institutions places black students at greater risk of leaving physical science majors (2.4 to 1). Degree-Field Transitions Of the few minorities abandoning engineering after the baccalaureate degree, a few switch to doctoral programs in the physical sciences (7.4%), with more switching to non-S/E doctoral programs (e.g., professions/other [6.7%] and education [6.3%]). Whereas white students usually change from one S/E subfield to another, high percentages of minorities switch from the life (42%) and physical sciences (36%) entirely to non-S/E doctoral majors such as education. Thus, minorities do not make the usual transition from the physical sciences to engineering, nor are all minority undergraduate life scientists shifting to medical careers. Factors Associated with Field Switching The reasons for field switching are slowly emerging. Because information about the undergraduate years is scant, this section draws heavily on Hewitt and Seymour’s (1991) ethnographic study, which focused on the attrition of well-qualified engineering students during their first two years of undergraduate study. Hewitt and Seymour found that, apart from the problems of cultural alienation and stereotyping, no extraordinary set of factors distinguished the field switching or persistence behaviors of minority and white students. Having said that, they concede

252 Early Career Preparation: Academia table 4 Field-Switching Patterns of Black Ph.D.s Who Earned Physical Science B.A. Degrees, by Type of Undergraduate Institution and Sex, 1975–1990 (%) Type of institution and sex

Switchers

Non-switchers

81 62

19 38

63 42

37 58

TBIa Women Men TWIb Women Men

Source: National Science Foundation, Doctorate Records File, 1975–1990. aχ2

bχ2

= 22.7 df = 1 p = .000. Odds ratio = 2.675; TBI = traditionally black institutions. = 20.9 df = 1 p = .000. Odds ratio = 2.403; TWI = traditionally white institutions.

that the problems encountered by minority switchers from engineering were more pronounced. As an example, field switchers reported financial difficulties, inadequate high school preparation in basic subjects, difficulty in dealing with college classes and competitive grading, and the impersonal attitudes of faculty—although these views were more often voiced by minority students. Whereas white switchers attributed part of their failure to inadequacies in their S/E programs, minority switchers frequently blamed themselves by expressing more anxiety about their decision to switch and the fear of letting down their families and losing face in their communities. Low grades also contributed to the loss of otherwise able students, but these were particularly significant in explaining the loss of minority students. Race/ethnic differences. Hewitt and Seymour found subgroup differences in the perceptions, expectations, and reactions to S/E majors among minority students. Some black switchers had academic problems but failed to use tutorial services provided by minority programs. Some Hispanic students reported switching majors because they did not feel adequately prepared by their high schools’ science and technical courses and felt they were already behind when they entered college. Even Hispanic students with respectable grades felt inadequate in highly competitive S/E classroom environments. Instead of seeking help from faculty, peer support was sought for help with academic problems. Asian American switchers felt driven into science and math by the expectations of teachers and parents. Others switched because they were uncomfortable with the “successful minority” image and wrestled with feelings of failure for not living up to this expectation. American Indian students found that their mathematics preparation was seriously deficient. They were reluctant to question authority and did not mix well with other students socially or in study groups. For others success in S/E majors came at the expense of family and cultural identity. Nonswitchers dealt with feelings of cultural alienation either by relying on American Indian support groups

The Preparation of Minorities for Academic Careers in Science and Engineering 253

or early marriage. Thus, although minority switchers had different reasons for switching from engineering, except for Asian Americans one reason was common to all groups: the failure to get adequate help when they encountered academic problems. Less is known about the reasons why minority students change fields after the baccalaureate degree. Reasonable speculations certainly include self-selection behaviors (e.g., choosing to teach rather than to become scientists) and differences in preparedness for graduate study. But evidence also points to social selection processes that may be at work. As an example, black students still report being rejected or discouraged from applying to graduate S/E programs because of lower expectations expressed by faculty and peers. Studies, however, show that minority students successfully complete graduate programs even when they do not meet traditional admissions criteria (Hartnett and Payton 1977, Pruitt and Isaac 1985). Summary The literature on field switching reveals several facts about S/E minorities: • Minority attrition rates are higher than those of white and Asian students in all S/E majors. • Regardless of race/ethnic or sex identification, attrition rates are highest for the physical and life sciences; minority women account for most losses in these majors. • Black students, particularly black women, have higher attrition rates than other race/sex groups in the physical sciences. • Black student attrition in the physical sciences is linked to attendance at TBIs, where a higher proportion of the women and men switch majors. • The reasons for minority student attrition include failure to use tutorial services, inadequate high school preparation for college S/E courses, feelings of inadequacy, pressure from others to major in S/E degree fields, and the incompatibility of science in relation to cultural values and self-identity. These findings indicate that the access and retention war is being lost at both ends of the pipeline. A promising note is that when the number of minorities in engineering expands, the number of minority engineering Ph.D.s and faculty may be substantially enlarged because the undergraduate-to-graduate retention rate in this field is higher than in other S/E career fields. Losses in the life and physical sciences, however, present a bleak picture and raise questions about the barriers confronting minority students.

What Research Can Tell Us About Barriers to Advancement in S/E Education National statistics generally understate the importance of factors that reduce the production of S/E minority Ph.D.s and ultimately prune the number of potential minority faculty. Without qualitative data, some questions are difficult if not impos-

254 Early Career Preparation: Academia

sible to answer. For example, is the study of science or career choice influenced by gender and race or ethnicity? Does success in science depend on the choice of discipline? Do issues such as competition versus collaboration, isolation, faculty–student relationships, and the racial/gender composition of the faculty and student body affect completion rates? A better question may be: Why can’t research answer these questions? The research literature is silent on these issues for several reasons. First, minorities are sparse in S/E education and therefore do not garner the attention of mainstream researchers. There is little substantive interest in what some refer to as “narrow issues,”3 even though questions are continually raised by policy makers, academics, and researchers concerning the underrepresentation of minorities in S/E careers. Second, although case studies and ethnographic research can provide substantive answers, funding agencies are less inclined to support data-collection methods that are labor-intensive and less cost-effective. This section and the next present what is known about the daily experiences of minority men and women in S/E education. Undergraduate Education Studies indicate that a subtle form of racism is pervasive in S/E departments. In general, minority students are convinced that white students and faculty are prejudiced against them; in the case of some minority students, these feelings influence their choice of institution. For instance, Rayman and Brett (1993) found that black undergraduate women in S/E majors preferred to attend TBIs because they believed that racial discrimination was much more prevalent than sexual discrimination in colleges and universities (95% vs. 25%, resp.). Hewitt and Seymour (1991) also found a subtle form of racism to be rooted in the hostility that minority students reported they “sensed” and “felt” within their environments rather than in overt forms of racial discrimination. These researchers concluded that minority students are correct in sensing antagonism from white students because in focus groups white students expressed resentment and hostility toward minority students not because of their presence in S/E majors but because of financial aid programs that were targeted at minorities. Their animosity was particularly aimed at students of doubtful minority status who did not “look like a minority” (e.g., some American Indians) and at affirmative-action practices that opened opportunities for minorities in postgraduation hiring. Hewitt and Seymour concluded that there was evidence to support the recent Carnegie Foundation (1990) findings that the climate of racial hostility is overt and growing on American campuses. Thus, in addition to concerns about strained faculty–student relationships and insensitivity, undergraduate minorities perceived a lack of peer support in their S/E departments. Graduate Education We know less about the experiences of minorities in graduate S/E education. The few studies that exist on minority students in graduate S/E majors show that the perceptions of graduate students are similar to those of undergraduates. Most studies of minority students in graduate education address educational issues concerning ac-

The Preparation of Minorities for Academic Careers in Science and Engineering 255

cess, retention, and the racial climate in departments. Not all of the studies discussed in this section describe these issues for S/E departments, but the findings are pertinent because they are strikingly similar to findings on minorities in S/E graduate education (Brown 1994). Recruitment. Relatively few minority graduate students believe their departments are engaged in recruitment activities. Thomas, Clewell, and Pearson (1992) found that graduate minority recruitment is more effective when activities are based in graduate deans’ offices. Effective recruitment efforts (1) provide funds for departments to recruit minority students, (2) sponsor recruitment conferences, fairs, and summer research opportunities for undergraduates, and (3) participate in exchange programs to obtain the names and information about promising minority candidates. Identification and personal contact with minority students were found to be among the most effective recruitment practices (Adams, 1989; Blackwell, 1983; National Board on Graduate Education, 1976). Admissions practices. More research is needed on graduate admissions decisions in S/E departments (National Board on Graduate Education 1976; Pruitt and Isaac 1985; Thomas, Clewell, and Pearson 1992). What is known indicates that minority graduate students are unaware of the criteria used for their admission to S/E programs (Brown 1994). Thomas, Clewell, and Pearson (1992) found that none of the departments in their study had special committees for minority students. Although departmental standards are higher than graduate school standards, exceptions are often made for both minority and majority students, especially for promising minority students with low GRE scores. Financial aid. Financial indebtedness is a major deterrent to minority persistence at the graduate level (Hauptman 1986, Nettles 1988, Trent and Copeland 1987). The amount, type, and duration of financial aid is important to both access to and completion of S/E graduate education. The availability of financial aid has declined in recent years, with a marked decrease in scholarships and grants (Nettles 1987). Financial support in the form of fellowships and university subventions is particularly crucial to S/E doctoral completion rates of black and Hispanic students (Baker 1994, Brown 1995a). Yet more American Indian, black, and Mexican American S/E doctorates support their graduate education through personal resources and loans than do those in these groups who receive support from university funds (Brown 1990, 1991; Thurgood and Weinman 1991). By contrast, most Asian Americans receive university funds as their primary source of support and rarely borrow against their future incomes in the form of loans. Although Puerto Rican and other Hispanic S/E doctorates receive university funding that is comparable to that of white doctorates, they have higher cumulative debt rates than other minorities (Brown 1995b). When minority students receive university funding, the primary source of these funds is in the form of set-aside programs (e.g., university and minority fellowships) even though department administrators are encouraged to nominate minority students for all university awards (Thomas, Clewell, and Pearson 1992). Brown (1995a) found that, regardless of subgroup,

256 Early Career Preparation: Academia

the likelihood of receiving a National Science Foundation fellowship is lowest for minority S/E applicants, and that without an award minorities were significantly less likely than whites to complete the doctorate in a timely manner. Finally, the funding sources for minorities pale in comparison to similar sources received by foreign students. Foreign S/E doctoral students in U.S. institutions receive 60 to 70 percent of their financial support from university funds (Brown and Clewell 1991). While this is not to suggest that university funds should not be allocated to foreign students, the amount they receive seems excessive in light of cutbacks in federal funds and guaranteed student loans for U.S. students. Racial climate. The relationship between faculty and students is directly related to the completion rates of all graduate students. For minority students, however, relationships are complicated by problems both in and out of class (Baird 1973; Brown 1994; Duncan 1976; Girves and Wemmerus 1986; Thomas, Clewell, and Pearson 1992). One study (Duncan 1976) that measured minority student satisfaction found that during the 1970s up to 80 percent of minority students were dissatisfied with their relationships with faculty. Although the level of dissatisfaction appears to have diminished, little has changed. Research indicates that both minority and white students are concerned about the amount of overt and subtle racism on their campuses (Thomas, Clewell, and Pearson 1992). A subtle brand of racism is particularly manifested in lower faculty expectations of minority students, insensitivity, and the failure of faculty to understand minority students’ needs (Brown 1994; Duncan 1976; Hall and Allen 1982; Thomas, Clewell, and Pearson 1992). Interviews with minority and white faculty reveal that some faculty and staff lack sensitivity to and an awareness of minority students’ concerns, and that on many campuses improvement in the departmental climate is a growing necessity in the area of race relations. Retention. The relationship between minority students and faculty appears to be directly linked to retention (Girves and Wemmerus 1986, McBay 1986, Pruitt and Isaac 1985, Wright 1964). Even though effective minority retention programs have been identified (Clewell 1987, Hamilton 1973), few formal programs exist, nor are there many effective retention practices in operation in most graduate departments. Thus, it is impossible to accurately track the magnitude of minority student losses because many departments fail to keep formal data on minority student retention (Thomas, Clewell, and Pearson 1992). Perceptions and Experiences of Minority Women in S/E Education Minority women are the most underrepresented group in the fields of science and technology. According to Irvine minority women in higher education “face unique and complex problems that many Black men, few White women, and few White men understand.” Yet there is almost no research on whether minority women are advantaged or disadvantaged in S/E education. For example, Campbell (1992) points out that although minority women appear to fare well in engineering, they are still

The Preparation of Minorities for Academic Careers in Science and Engineering 257

underrepresented (1.9%) among engineering baccalaureate graduates. The lack of information raises an important question: Are minority women in a double bind or do they receive double the advantages in science? The existing literature is suggestive but does not answer this question. For example, Garrison (1987) found that minority women were falling through the cracks in the government’s bifurcated efforts to increase minorities and women in scientific-degree programs. Moreover, Brown (1994), Leggon (1987), and Tolbert (1993) found significant disparities in the representation of minority women in S/E doctoral fields. Even when minority women are fully funded, they take longer to complete the doctorate than minority men (Brown 1994). Other than statistics on participation rates, salaries, and academic rank, which show remarkable differences between minority women and other groups, there is almost nothing on the career advancement of minority women scientists in the academic workforce. Although there is a growing interest in working minority women scientists in the general labor force (Adler 1994; Brown 1994; Campbell 1992; Koelewijn-Strattner 1990; KoelewijnStrattner, Adler, and Lengermann 1991; Malcom, Hall, and Brown 1975; Tolbert 1993), less effort has been exerted to sort out the educational barriers that deter minority women from pursuing science degrees. This is particularly problematic because, as we have seen with faculty-status issues and field switching, the problems confronting minority women are generally glossed over or buried in statistics on minorities and women. Minority women are overlooked in science research for several reasons: their numbers are small; there is a greater focus on the problems of minority men in higher education; and there is a historical dilemma over how to consider minority women in research (e.g., as either women or minorities, or as members of both, since they share the concerns of both groups). Brown’s recent studies (1994, 1995a) address the concerns of minority women by describing the experiences that act as barriers to the training of minority women in graduate S/E education. These studies attempt to clarify the influence of race and sex on the education of minority women by describing, based on firsthand accounts, shared perceptions and experiences (Brown 1994) and by determining how well they fare in the NSF graduate fellowship award process (Brown 1995). Perceptions of Minority Women Minority women who successfully gained access to or graduated from S/E graduatedegree programs gave positive responses to survey questions about the structural sources of advantages and disadvantages in their programs (Brown 1994). Nonetheless, several structural disadvantages were noted in their answers to both survey and interview questions. Recruitment. Similar to Thomas, Clewell, and Pearson’s findings (1992), few S/E respondents said they were recruited by graduate programs. Over 80 percent of minority women and men believed that the lack of recruitment on the part of their graduate programs presented minority students with a major disadvantage over other students in S/E graduate majors.

258 Early Career Preparation: Academia

Faculty composition and role models. Minority women believed their graduate school experience was disadvantaged by the lack of faculty role models. They expressed greater concern about factors related to the racial and gender composition of the faculty and the racial climate in class than did minority men and white women who responded. Minority women in engineering and the physical sciences (78%) were more likely to indicate that they were disadvantaged due to these factors than their counterparts in the life sciences (36%). Tutorial and counseling/advising support. Differences were apparent among students as to how respondents perceived disadvantages about formal tutoring and counseling/advising services. White women respondents expressed more dissatisfaction with tutorial services (43%) than minority women (33%) or minority men (24%). White women were also more dissatisfied with counseling services than minority respondents. Teaching assistantships (TA) and research assistantships (RA). TAs and RAs are required for all graduate students in many programs. But the students said the selection process was neither “random nor routine.” Minority women (22%) believed they were much more disadvantaged by the RA selection process than white women (14%) or minority men (8%). Affirmative-action programs. Sharp differences in opinion between minorities and white women respondents were expressed about the advantages and disadvantages of affirmative-action programs. Nearly all minority respondents (96%) stated that affirmative action was necessary to promote and advance minorities in their field. By contrast, white women were much less inclined (49% vs. 96% for minorities) to endorse affirmative action in order to increase the number of women and minorities in S/E graduate education. Interpersonal Sources of Disadvantage Interpersonal relationships—more than structural barriers—were the prime source of disadvantage for minority women respondents in the form of discouragement, dissatisfaction, and isolation. Isolation. Minority women chose graduate programs according to the program’s location (e.g., near family, friends, and large populations of people from their own race/ethnic group) in order to avoid the isolation found on major campuses with wellestablished S/E graduate programs. Location was more important for minority women (55%) than for minority men (32%) or white women (21%). Racism. Overt racism is rarely expressed in class, but when it is minority women (17%) feel they are more disadvantaged by open racism than minority men (4%) or white women (3%). Subtle racial prejudice appears to be more commonplace in the classroom, and both minority women and minority men (34%) said they were mistreated by faculty members due to their race.

The Preparation of Minorities for Academic Careers in Science and Engineering 259

Sexism. Sexism was a daily reality for all of the women respondents, both minority and white. However, substantially fewer minority women than white women (9% vs. 31%, resp.) complained of obvious sexism. Subtle sexism, similar to subtle racism, is much more common. Slightly more minority women than white women (34% vs. 29%, resp.) complained about subtle forms of sexist behavior in class. Significance of race/ethnic identifiability. Respondents identified themselves as recipients of racist attitudes and behaviors depending on whether they were “identifiable” minorities. Nonidentifiable minority women, particularly Hispanics, complained more about sexist attitudes and behaviors than did identifiable minorities. Black women and the dual-status dilemma. More than any other group, black women believed that race was the deciding factor in how they were treated by faculty and other students. Yet racism and sexism sometimes posed a conundrum for black women, who could not always distinguish between racist or sexist behaviors in class. Black women believed they faced a triple dilemma: they had to succeed on a personal level, because they were black, and because they were women. Faculty-student relationships. Minority women expressed greater dissatisfaction with faculty–student relationships and the inaccessibility of faculty than did minority men or white women. Substantially more minority women and men reported feelings of nonacceptance in class (45% and 52%, resp.), as compared to white women (5%). Both white women (38%) and minority women (34%) reported feeling uncomfortable in class due to their sex. Few minority women had true mentors while in graduate school, but those who did reported exceptional relationships and experiences. Isolation within programs. Minority women were isolated in S/E graduate programs. White students were often described as arrogant and indifferent, while minority men were said to treat minority women as intellectual inferiors. Yet social segregation was shared by both minority women and minority men; few were able to form collaborative study relationships with white students. National Science Foundation (NSF) Fellowship Awards NSF fellowships are awarded to approximately 950 applicants each year. These fellowships are offered to individuals who have the ability and aptitude for advanced study in science leading to a doctoral degree. In 1979 the Minority Graduate Fellowship Program (MNSF) was created to increase the participation of more minorities in science careers by providing them with sufficient funding to seriously pursue graduate study. Data from NSF fellowship programs were used to examine the nature and extent of inequality based on race and gender in the NSF allocation process (Brown 1995). An analysis of comparable applicants to the regular NSF fellowship program revealed several important findings. First, minority women had the lowest probability of being rated truly exceptional or outstanding by referring scientists and faculty. Their scores were lower than those of minority men, white women, and white men.

260 Early Career Preparation: Academia

Second, being a minority woman further reduced the chances of winning a fellowship as applicants moved from one decision-making point to the next. For example, minority women are significantly less likely to receive high panel rating averages, to be placed in the top two quality groups (from which awardees are selected), or to receive offers of fellowships. Third, minority women are also significantly less likely to attain the doctorate irrespective of whether or not they received an NSF fellowship. These findings held up even when adjustments were made for program, GRE scores, UGPAs, and degree field. A test for the joint influence of race and gender on award status and Ph.D. attainment outcomes revealed that being a minority and a woman significantly reduced the odds of receiving a fellowship award and attaining the doctorate for underrepresented minority women. Summary The literature in this section reveals that the barriers to S/E education include: • unreceptive racial climates in undergraduate and graduate departments • inadequate and ineffective recruitment practices for advanced study • nonexistent special-admission committees to address minority-admission concerns • a higher degree of indebtedness and less university financial aid for minority students • ineffective minority-retention programs, including a lack of formal data to track minority-student retention • minority women who perceive their race and sex to be linked to their double-minority status.

Filling in the Gaps: New Research and Policy Directions I began this chapter by asking two questions: Why are there so few minority faculty in S/E departments? What can be done to alleviate minority shortages on S/E faculties? While the literature sheds light on some issues, the available information does not adequately answer these questions. Thus, this section presents additional questions and issues requiring new answers and solutions. Minority Faculty Minority faculty shortages in science and engineering are very real and require new policy recommendations and avenues of research. The following questions demand answers: • To what extent are minority–white discrepancies due to differences in preparation, work history, and self-selection factors (e.g., preferences for nonacademic versus academic career choices)?

The Preparation of Minorities for Academic Careers in Science and Engineering 261

• How do minority S/E faculty perceive their status, role, and career opportunities in academic work settings? • What hinders collegial relationships among minority and white faculty in TWIs? • What prevents minority faculty from achieving their professional goals in TWIs? • Do minority faculty perceptions of status, role, and career opportunities differ by S/E career field? • How is affirmative action being implemented to increase minority faculty in S/E careers? Research Directions and Policy Recommendations Because little or no information exists on minority faculty workforce issues (e.g., rank, salary, and the quality of the work environment), the reasons for these discrepancies are uncertain. For example, it is unclear whether the doctorate leverages the status of minority faculty in the same way that it does for white and Asian faculty. The answers to such questions can be obtained only through more rigorous survey and qualitative studies. Federal and private research-funding priorities should include the following: (1) large systematic studies on the career directions of minority Ph.D.s to determine how self-selection versus social-selection processes channel minorities toward academic or nonacademic careers; and (2) studies of minority groups using qualitative methods to get at substantive issues concerning career paths, ceilings, and directions (academic vs. nonacademic) in S/E fields. In addition, career choice may be related to the lack of minority-faculty role models. To address this issue active minority faculty recruitment is critical to the diversification of S/E faculty. For example, minority students believe that more than one minority faculty member should be recruited at a time to prevent the “fishbowl” experience of being the only “one” with no support system when minority faculty arrive on campus. By having more than one minority faculty member on staff, students believe that such faculty members might be retained for more than just a few years (Brown 1994). Moreover, given the sparse pool of minority Ph.D.s for S/E academic careers, minority-faculty recruitment should be initiated while minority candidates are still in graduate school to ensure that real job offers will be available upon completion of the doctorate. Institutional monitoring of career patterns, pathways, and advancement might help determine the validity of these solutions. Field Switching The high attrition rate in S/E education—due, in part, to field switching—has lessened the gains in minority student enrollments while also raising the following questions: • What determines field-switching patterns at specific points (e.g., before and after completing the B.S.) in the educational system? • How does engineering retain minority students during the undergraduateto-graduate transition levels?

262 Early Career Preparation: Academia

For example, we do not know how many minority engineers with baccalaureate and master’s degrees fail to pursue the doctorate or earn master’s degrees in other degree fields. There is evidence that instead of pursuing engineering doctorates, minority engineers may be pursuing master’s degrees in M.B.A. programs.4 Thus, more research is needed on the postbaccalaureate work experiences of engineering students. For example, it is important to know (1) why minority attrition from the life sciences and physical sciences is so high, particularly for black students, and why the loss rates are higher for minority women; (2) what role social and intellectual isolation play in the decision to switch degree majors; and (3) what role faculty–student relationships play in the decision to switch majors among well-qualified undergraduate minority students. Research Directions and Policy Recommendations Three concerns are related to minority student retention in S/E majors: recruitment, adequate funding, and establishing formal methods of monitoring retention rates. One policy issue in need of change is the failure to keep data on student attrition rates. Neither the magnitude nor the rate of minority student attrition can be understood apart from the careful monitoring of minority retention in S/E majors. Systematic data are needed on attrition rates and patterns, particularly at the undergraduate level. To retain students in S/E majors, activities should be provided to increase interaction between minority faculty and minority students (Thomas, Clewell, and Pearson 1992). Brown (1994) recommends providing student scholarships to attend minority S/E conferences so that students are exposed to minority role models. These conferences help minority students learn the conventions of members of their profession, learn to network with other professionals, and provide an outlet for the dissemination of their current research. National gatherings also provide the only collective forum where minority women see other minority women in their particular subfields. Set-aside funding for minority students should continue, but graduate deans should also negotiate with departments to award more research assistantships to minority students. Graduate deans should provide incentive grants to faculty who are willing to mentor and train minority students as researchers (Brown 1994; Thomas, Clewell, and Pearson 1992). Effective retention strategies have been demonstrated by such exemplary programs for minority scientists and engineers as the NACME, GEM, and Florida McKnight fellowships programs. Beyond financial support, research is needed on what these organizations are doing to retain minority students in the graduate pipeline and how some strategies might be adapted by higher education institutions. Barriers to S/E Education The barriers to S/E education raise several questions: • What can be done to promote minority inclusion in S/E education, particularly minority women?

The Preparation of Minorities for Academic Careers in Science and Engineering 263

• How can racial and gender climates be administratively changed and structured to improve the learning environment for all students? • What can be done to increase support for qualitative research on issues such as faculty–student relationships and peer support systems? Research Directions and Policy Recommendations Several recommendations offered for field switching apply to barriers encountered in S/E education. These include changing student recruitment and admissions procedures and improving faculty–student relationships and departmental and classroom climates. Minority students in master’s degree programs represent a viable pool for recruitment into doctoral programs. The involvement of the students in planning and activities should enhance recruitment efforts. It is also essential to recruit more than one minority student at a time to keep minorities in the S/E pipeline. This adjustment will provide natural peer support for such activities as study groups, which are essential to performing well and retaining minorities in technical degree fields. To increase enrollment, a flexible and creative use of admissions criteria (such as GRE scores) should stress inclusion rather than exclusion. Minority affairs committees should be established in all graduate admissions departments and both shortand long-term evaluations of the success or failure of minority students should be conducted in order to measure the effectiveness of different admissions criteria. To combat racism, faculty should be educated about the consequences of prejudice and racism. Faculty sensitive to the needs of minority students should be hired to work with students of different races and sexes. More summer internships are needed to improve faculty–student working relationships; moreover, government research contracts requiring the training of minority students, particularly minority women, will make faculty accountable for what students actually learn on research projects. Cooperation among S/E students can be achieved through a structured decrease in student competition. Faculty-designated study groups will eliminate race and sex cliques while fostering new and more viable cooperative relationships.

Conclusion The shortages of minorities in the academy offer rare opportunities to promote and upgrade the training of minorities and set new faculty-staffing priorities. Making institutional adjustments requires vision by galvanizing the attention of faculty, educational administrators, and government policy makers. Currently colleges and universities are ineffective in marketing the material and professional benefits of academic careers in the S/E fields. Many minorities view the academic sector as a single career track leading to teaching. Faculty must do a beter job of exposing high school students and undergraduates to the wide range of opportunities open to them in academe (e.g., pure, applied, and policy research; administration; consulting). We

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can reap the benefits of more minorities in S/E academic careers and maintain an aggressive training program for minority students if we recognize that the two are linked. Thus, current literature suggests that in addition to training the human capital to serve the government, the corporate world, and the professions, it is important to understand how higher educatin can set new priorities to revitalize its own teaching and research faculty.

Notes 1. This percentage refers to S/E doctorates in the life sciences, physical sciences, and engineering. When the social and behavioral sciences are included, the percentage of underrepresented minorities increases to 6 percent. 2. Jackson’s measures of institutional quality included the ratio of graduate to undergraduates, books to faculty, and the quality (high, medium, low) of admissions standards. 3. This phrase, used by Senator Alan Simpson of Wyoming, typifies the attitude of researchers who ignore the issues of minorities in science. It also reflects the priorities of funding agencies in their decisions to allocate funds for scientific research. 4. In my current research on minority scientists and engineers in the workforce, I am running into a number of minority women and men engineers who are pursuing or planning to pursue master’s degrees in graduate business programs. Their reasoning for earning an M.B.A. graduate degree is that it will increase their employment opportunities in the workforce.

Selected References Adams, H. G. 1989. Minority students shortchanged. Black Issues in Higher Education 5: 22–25. Adler, M. A. 1994. Male–female power differences at work: A comparison of supervisor and policy makers. Sociological Inquiry, 64(1): 37–55. American Association for the Advancement of Science. 1989. Project 2061: Science for all Americans. Washington, DC: American Association of American Science. American Chemical Society. 1992. Domestic status, discrimination, and career opportunities of men and women chemists. Washington, DC: American Chemical Society. Anderson, M. L. 1988. Thinking about women. New York: Macmillan. Baird, L. L. 1973. Careers and curricula: A report on the activities and views of graduates a year after leaving college (GRE Board Research Report No. 79–11R and ETS Research Report 82–53). Princeton, NJ: Educational Testing Service. Baker, J. G. 1994. Gender, race, and progression to the natural science and engineering doctorate. Report to the Directorate for Education and Human Resources, National Science Foundation. Beale, F. 1979. Double jeopardy: To be black and female. In T. Cade, ed., The black woman: An anthology, New York: Mentor Books. Berryman, S. E. 1983. Who will do science? Trends and their causes in minority and female representation among holders of advanced degrees in science and mathematics. New York: The Rockefeller Foundation. Betz, N. E. 1990. What stops women and minorities from choosing and completing majors in science and engineering. Washington, DC: Federation of Behavioral, Psychological, and Cognitive Sciences.

The Preparation of Minorities for Academic Careers in Science and Engineering 265 Blackwell, J. G. 1983. Networking and mentoring: A study of cross-generational experiences of Blacks in graduate and professional schools. Atlanta, GA: Southern Education Foundation. Bock, E. W. 1969. Farmer’s daughter effect: The case of the Negro female professionals. Phylon 30: 17–26. Brown, S. V. 1987. Minorities in the graduate education pipeline. Princeton, NJ: Educational Testing Service. ——— . 1988. Increasing minority faculty: An elusive goal. Princeton, NJ: Educational Testing Service. ——— . 1989. A study of losses in the education pipeline and science talent pool. Report prepared for the Government-University-Industry Roundtable. Washington, DC: National Research Council, National Academy of Sciences. ———. 1990. Minorities in science and engineering education. Paper presented at the symposium “Beyond Decrees: A Centennial Commitment to Cultural Diversity in Education,” Center for Research on Minority Education, The University of Oklahoma, Norman, Oklahoma. ———. 1994. Underrepresented minority women in graduate science and engineering education (Final Report). Princeton, NJ: Educational Testing Service. ——— . 1995a. The underrepresentation of minority women in science and engineering education (Final Report submitted to the National Science Foundation). Princeton, NJ: Educational Testing Service. ——— . 1995b. Profiles and persistence of minority doctorate students (Final report submitted to the Graduate Records Examination Board). Princeton, NJ: Educational Testing Service. ——— . 1996. The changing profile of college students: New challenges, new demands. In R. Hope and L. Rendon, eds., Educating for the new majority. San Francisco: Jossey-Bass. Brown, S. V., and B. C. Clewell. 1991. Building the nation’s workforce from the inside out: Educating minorities of the twenty-first century. Norman, OK: Center for Research on Multi-Ethnic Education. Campbell, G. 1992. The gender gap in minority engineering education. Research Letter 3(1). Carnegie Foundation for the Advancement of Teaching. 1989. The condition of the professoriate: Attitudes and trends. Princeton, NJ: Educational Testing Service. ——— . 1990. Campus life: In search of community. Princeton, NJ: Educational Testing Service. Chipman, S., and V. Thomas. 1984. The participation of women and minorities in mathematical, scientific, and technical fields. Review of Research in Education 14: 387–430. Clewell, B., and B. Anderson. 1992. Breaking the barriers. San Francisco: Jossey-Bass. Cole, J. R. (1979). Fair science. New York: The Free Press. Cole, J. R., and S. Cole. 1973. Social stratification in science. Chicago: University of Chicago Press. Committee on International Exchange and Movement of Engineers. 1988. Foreign and foreign-born engineers in the United States. Washington, DC: National Academy Press. Digest of Education Statistics. 1993. 32nd ed. National Center for Education Statistics. Washington, DC: U.S. Government Printing Office. Drucker, D. C. 1988. On foreign engineering in academe. In Committee on International Exchange and Movement of Engineers, Foreign-born engineers in the United States. Washington, DC: National Academy Press. Duncan, B. L. 1976. Minority students. In J. Katz and R. T. Hartnett, eds., Scholars in the main. Cambridge, MA: Ballinger.

266 Early Career Preparation: Academia Dunteman, G., J. Wisenbaker, and M. E. Taylor. 1979. Race and sex differences in college science program participation. Research Triangle Park, NC: Research Triangle Institute. Duran, R. P. 1987. Hispanics’ precollege and undergraduate education: Implications for science and engineering students. In L. Dix, ed., Women: Their underrepresentation and career differentials in science and engineering. Washington, DC: National Academy Press. Epstein, C. F. 1973. Positive effects of the multiple negative: Explaining the success of Black professional women. American Journal of Sociology 78: 912–923. Faia, M. A. 1975. Productivity among scientists: A replication and elaboration. Sociological Review 40: 825–829. Fichter, J. H. N.d. Graduates of predominantly Negro colleges: Class of 1964 (Public Health Service Publication No. 1571). Washington, DC: U.S. Government Printing Office. Garrison, H. 1987. Undergraduate science and engineering education for Blacks and native Americans. In L. Dix, ed., Minorities: Their underrepresentation and career differentials in science and engineering. Washington, DC: National Academy Press. Garza, H. 1988. The “barriorization” of Hispanic faculty. Educational Record 69(4). Girves, J. E., and V. Wemmerus. 1986. Developing a model of graduate student degree progress. Paper presented at the annual meeting of the Association for the Study of Higher Education, San Antonio, TX. Green H. W. 1946. Holders of doctorates among American Negroes. Boston: Meador. Hartnett, R. T., and B. F. Payton. 1977. Minority admissions and performance in graduate study: A preliminary study of fellowship programs of the Ford Foundation and the Danford Foundation. New York: Ford Foundation, Office of Report. (ERIC ED 161 979). Hauptman, A. M. 1986. Students in graduate and professional education: What we know and need to know. Washington, DC: Association of American Universities. Hewitt, N., and E. Seymour. 1991. Factors contributing to high attrition rates among science, mathematics, and engineering undergraduate majors. Report to the Alfred. P. Sloan Foundation. Hill, S. 1990. Blacks in undergraduate science and engineering education (NSF–92–305). Washington, DC: National Science Foundation. Irvine, J. J. The Black female academic: Doubly burdened or doubly blessed? In P. A. Stringer and J. Thompson, eds., Stepping off the pedestal: Academic women in the South. New York: Modern Language Association. Jackson, K. W. 1988. A profile of Black faculty in traditionally white institutions. A report submitted to the Southern Education Foundation, Atlanta, GA. Jay, J. M. 1971. Negroes in science: Natural science doctors, 1876–1969. Detroit, MI: Balamp. Koelewijn-Strattner, G. 1990. Race, gender, and the scientific professions: Double negative or double jeopardy. Master’s thesis, University of Maryland, College Park, MD. Koelewijn-Strattner, G., M. A. Adler, and J. Lengermman. 1991. Race and gender in the chemistry profession: Double jeopardy or double negative. Paper presented at the annual meeting of the American Sociological Association, Cincinnati, OH. Leggon, C. 1980. Black female professionals: Dilemmas and contradictions of status. In L. Rodgers-Rose, ed., The Black woman. Beverly Hills, CA: Sage. ——— . 1987. Minority underrepresentation in science and engineering graduate education and careers. In L. Dix, ed., Women: Their underrepresentation and career differentials in science and engineering. Washington, DC: National Academy Press. Lorber, J. 1984. Women physicians. New York: Tavistock. McBay, S. M. 1986. The racial climate on the MIT campus. Massachusetts Institute of Technology, Office of the Dean for Student Affairs. Malcom, S. M., P. Q. Hall, and J. W. Brown. 1975. The double bind: The price of being a

The Preparation of Minorities for Academic Careers in Science and Engineering 267 minority woman in science (AAS Pub. 76–R–3). Washington, DC: American Association for the Advancement of Science. Maxfield, B. D. 1981. Science, engineering, and humanities doctorates in the United States. Washington, DC: National Academy Press. Merton, R. K. (1957). Social theory and social structure. Glencoe, IL: Free Press. Mestre, J. 1986. The Latino science and engineering student: Recent research findings. In M. A. Olivas, ed., Latino college students. New York: Teachers College Press. Moore, W., Jr. 1988. Black faculty in white colleges: A dream deferred. Educational Record 69(1): 116–121. Moore, W., and L. Wagstaff. 1974. Black educators in White colleges. San Francisco: JosseyBass. National Academy of Science. 1987. Nurturing science and engineering talent: A discussion paper. National Board on Graduate Education. 1976. Minority group participation in graduate education. Washington, DC: National Academy of Science, Printing and Publishing Office. National Center for Education Statistics (NCES). 1993. Digest of education statistics, 1993. Washington, DC: U.S. Department of Education. National Science Foundation (NSF). 1986. Undergraduate science, mathematics and engineering education. NSB Task Committee on Undergraduate Science and Engineering Education. Washington, DC: NSF. ——— . 1988, 1989a. Changing America: The new face of science and engineering. Interim and Final Reports, The Task Force on Women, Minorities, and the Handicapped in Science and Technology. Washington, DC: NSF. ———. 1989b. Meeting the national need for scientists to the year 2000. Commission on Professionals in Science and Technology. Washington, DC: NSF. ———. 1989c. Foreign students account for most growth in graduate science and engineering enrollment. Science Resources Studies. Washington, DC: NSF. ———. 1989d. Report on the NSF disciplinary workshops on undergraduate education. Washington, DC: NSF. ———. 1989e. Future scarcities of scientists and engineers: Problems and solutions. Washington, DC: NSF. ——— . 1989f. Report on the National Science Foundation workshop on science, engineering and mathematics education in two-year colleges. Directorate for Science and Engineering Education; Division of Undergraduate Science, Engineering and Mathematics Education. Washington, DC: NSF. ———. 1990a. The state of academic science and engineering. Directorate for Science, Technology and International Affairs, Division of Policy Research and Analysis. Washington, DC: NSF. ——— . 1990b. Women and minorities in science and engineering. Washington, DC: NSF. ———. 1991. Early release of summary statistics on science and engineering doctorates. Washington, DC: Division of Science Resources Studies. ——— . 1994. Women, minorities, and persons with disabilities in science and engineering, 1994 (NSF 94–333). Arlington, VA: NSF. Nettles, M. 1987. Financial aid and minority participation in graduate education. In J. C. Baratz-Snowden, S. V. Brown, B. C. Clewell, M. T. Nettles, and L. Wightman, eds., Research agenda for Graduate Record Examinations Board minority graduate education project. Princeton, NJ: Educational Testing Service. ———. 1988. Financial aid and minority participation in graduate education. Princeton, NJ: Educational Testing Service.

268 Early Career Preparation: Academia ——— . 1990. Black, Hispanic, and White doctoral students: Before, during, and after enrolling in graduate school. Princeton, NJ: Educational Testing Service. Office of Scientific and Engineering Personnel. 1987. Women: Their under-representation and career differentials in science and engineering. Washington, DC: National Academy Press. Office of Technology Assessment (OTA). 1985. Demographic trends and the scientific and engineering workforce. Washington, DC: U.S. Government Printing Office. ——— . 1988. Educating scientists and engineers: Grade school to grad school. Washington, DC: U.S. Government Printing Office. ——— . 1989. Higher education for science and engineering: A background paper. Washington, DC: U.S. Government Printing Office. Pearson, W., Jr. 1985. Black scientists, white society, and colorless science. Mulwood, NY: Associated Faculty Press. Pearson, W., Jr., and H. K. Bechtel. 1989. Blacks, science, and American education. New Brunswick, NJ: Rutgers University Press. Pearson, W., Jr., and J. R. Earle. 1984. Race–gender variations in the demographic characteristics of doctoral scientists. Sociological Spectrum 4: 229–248. Pearson, W., Jr., and A. Fechter. 1995. Who will do science: Educating the next generation. Baltimore, MD: Johns Hopkins University Press. Porter, B. F., and R. Czuijko. 1986. Becoming a professional physicist: A statistical overview. Physics Today (June). Pruitt, A. S., and P. D. Isaac. 1985. Discrimination in recruitment, admission and retention of minority graduate students. Journal of Negro Education 54(4). Raftky, D. 1972. The Black scholar in the academic marketplace. Teachers College Record 74(2): 225–260. Rayman, P., and B. Brett. 1993. Pathways for women in the sciences. Wellesley, MA: Wellesley College Center for Research on Women. Rios Rodriguez, C. 1992. Hispanic student achievement program of study and gender differences in attitudes toward mathematics at the high school level: An exploratory study. Ph.D. diss., University of Massachusetts, Amherst. Rodriguez, C. M. 1993. Minorities in science and engineering: Patterns for success. Ph.D. diss., University of Arizona. Silver, J., R. Dennis, and C. Spikes. 1988. Employment sequences of Blacks teaching in predominantly White institutions. Atlanta, GA: Southern Education Foundation. Syverson, P. B., and L. E. Forster. 1984. New Ph.D.s and the academic labor market (Staff Paper No. 1). Washington, DC: National Research Council, Office of Scientific and Engineering Personnel. Taylor, J. H. 1955. The Negro scientist. Baltimore MD: Morgan State College Press. Thomas, G. E., B. C. Clewell, and W. Pearson, Jr. 1992. The role and activities of American graduate schools in recruiting, enrolling and retaining U.S. Black and Hispanic students (Final Report). Princeton, NJ: Educational Testing Service. Thurgood, L. 1989. Doctorate recipients from United States universities (Summary Report, 1988). Washington, DC: National Academy Press. Thurgood, L., and J. M. Weinman. 1991. Doctorate recipients from United States universities (Summary Report 1990). Washington, DC: National Academy Press. Tolbert, M. E. M. 1993. Minority women in science and engineering. Journal of the National Technical Association 66(2): 4–15. Trent, W., and E. Copeland. 1987. Effectiveness of state financial aid in the production of Black doctoral recipients. Atlanta, GA: Southern Education Foundation. Vetter, B. M. 1979. Data on the participation of minority women in the sciences. Paper

The Preparation of Minorities for Academic Careers Enhancing in Sciencethe and Research Engineering Base 269 presented at the conference on Professional Minority Women, University of California at San Francisco. ———. 1990. Who is in the pipeline? Paper presented at the annual meeting of AAAS, New Orleans. Washington, V., and W. Harvey. 1989. Affirmative rhetoric, negative action. (ASHE–ERIC Higher Education Report No. 2). Washington, DC: George Washington University. White, P. 1993. Women and minorities in science and engineering: An update. Washington, DC: National Science Foundation. Wilson, R. 1987. Recruitment and retention of minority faculty and staff. AAHE Bulletin 39: 11–14. Zuckerman, H. 1971. Women and Blacks in American science: The principle of the double penalty. Paper presented at the symposium on Women and Minority Groups in American Science and Engineering, California Institute of Technology. Zuckerman, H., and J. Cole. 1975. Women in American science. Minerva 13: 82–102.

cheryl b. leggon

Enhancing the Research Base Limitations of the Data On the individual level, data on the participation of individuals from underrepresented indigenous minority groups (e.g., African American, Native American, Puerto Rican, Mexican American/Chicano) in the science and engineering (S/E) labor force should be disaggregated by gender and class. Disaggregating the data by gender facilitates comparisons between genders within racial/ethnic groups as well as comparisons within genders between racial/ethnic groups. Differences within racial/ethnic groups can be as great as differences between racial/ethnic groups (Leggon 1987, 1991). Minority women tend to be subsumed either under the category “minority” or “female.” Just as we cannot generalize from data on (and the experiences of) white women to those of women of color, we cannot generalize from data on (and experiences of) men of color to those of women of color (Sokoloff 1992). Disaggregating the data by class within racial/ethnic (and gender) group is particularly important for at least two reasons. First, the literature points out the difficulty of lower SES parents in socializing their children to pursue careers requiring

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higher-order thinking (Calhoun, Light, and Keller 1994). Second, socioeconomic background has such a great impact on life chances that it is scientifically unsound to generalize from data (and experiences) based on individuals from one class background to those of another class background (Higginbotham and Weber 1992). Some researchers object to disaggregating the data because of the statistical problems associated with small cell sizes, while others object because of the confidentiality problems associated with small cell sizes. In my opinion, these methodological hurdles should not become barriers to our enhanced understanding of the issues being addressed, namely, underrepresented minorities in S/E careers. Another limitation of the data at the individual-level analysis concerns data on salaries in the academic and nonacademic marketplace. These data should reflect variations by rank and/or years of service. Anecdotal data suggest that the prevalence of non–U.S.-born faculty is a barrier to pursuing careers in S/E for white women. We know that there are many foreign-born faculty at several of the smaller private and public historically black colleges and universities (HBCUs). Extant data do not indicate to what extent the prevalence of foreign-born S/E faculty affects the recruitment and retention of male and female African American students in S/E (Leggon and Pearson 1994). More qualitative data are needed—ideally longitudinal in nature—to enable us to trace changes in minority scientists’ and engineers’ training and work experience. Often it is through experience and the benefit of hindsight that we are able to reevaluate just how good or ineffectual our training was. For example, a study on the careers of African American chemists suggests that minority and nonminority students at the same institution and in the same department can—and often do—have very different experiences (Pearson forthcoming). For example, not only do students of color tend to have fewer predoctoral publications than their white counterparts— or none at all—but they do not even realize this until they are near or at the end of their graduate training. At the institutional level, graduate departments should keep data on the following: • • • •

retention rates by race/ethnicity and gender time-to-degree by race/ethnicity and gender stop-out rates by race/ethnicity and gender distribution of departmental teaching assistantships (TAs) and research assistantships (RAs) by race/ethnicity and gender.

Just as some institutions of higher education keep records of the graduation rates of their athletes, departments should keep data on the retention rates and the timeto-degree of their students by race/ethnicity and gender. These data indicate which departments have track records for getting students through to degree. Similarly, departments should also collect data on the stop-out rates by race/ethnicity and gender. These data are important because research indicates that while working on advanced degrees individuals from indigenous non-Asian minority groups—especially women—tend to stop out, that is, take courses, stop for a while to work and save money, and then take more courses (National Research Council 1993). Moreover, this stop-out pattern is related to the fact that African Americans, Mexican Americans, and Puerto Ricans are more likely to finance their education from savings than

The Preparation of Minorities for Academic Careers Enhancing in Sciencethe and Research Engineering Base 271

from university assistantships (National Research Council 1991). This is another reason why departments should collect data on the distribution of departmental TAs and RAs by race/ethnicity and gender. Students receiving RAs are more likely to complete their degree (and in less time) than are their counterparts receiving TAs. If they receive university assistantships, minorities are more likely to receive TAs than RAs (National Research Council 1991). Departments should collect data disaggregated by race/ethnicity and gender on retention rates, time-to-degree, stop-out rates, and distribution of departmental TAs and RAs. These data should be available to students before they commit to a graduate program not only so that they can make an informed choice of a suitable graduate program but also so that they can have realistic expectations about their graduate experience (e.g., how long it is likely to take them to earn their degree). Moreover, these institutional data should include ethnographic and longitudinal data on the departments (e.g., detailed information on recruiting practices; admissions and financial aid policies; and formal and informal mentoring).

Gaps in Research We need to know more about the impact of undergraduate origin on the career experiences of African American scientists. Data on baccalaureate origins should be disaggregated by type of institution: predominantly white colleges and universities (PWCUs), HBCUs, and women’s colleges. In a recent study of the baccalaureate origins of African American female Ph.D. scientists (Leggon and Pearson 1994), it proved beneficial to disaggregate the undergraduate institutions as follows: PWCUs, HBCUs, women’s colleges, and predominantly black women’s colleges. The results indicated that among African American women earning social science doctorates between 1984 and 1992, more had baccalaureate origins in the two historically black women’s colleges—Spelman and Bennett—than they did in all Seven Sisters colleges. Moreover, two additions to the usual typology have been identified as being potentially fruitful: recently black colleges and universities (RBCUs), and recently predominantly Hispanic institutions (RPHIs) (Leggon and Etzkowitz forthcoming). More needs to be known about performance variables in graduate school. Specifically, we need to better understand differences between people of color and whites— further disaggregated by gender—in terms of the numbers of predoctoral publications. Predoctoral publications constitute a significant factor in first professional placement, which in turn affects subsequent placement (Pearson 1986). More needs to be known about the impact of outside funding. Just as we know that grants are useful in recruitment (but not retention), we need to know more about the impact of outside funding versus inside funding. For example, one of the unanticipated consequences of minority students having outside funding can be to lessen their probability of working with professors on major projects from which a student can “carve out a portion” for their dissertation. More needs to be known about the criteria used by minority students to select their graduate program. Shirley Vining Brown has said that geography is an important consideration for minority women so that they can discharge their obligations

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to their family of orientation and/or procreation. What other factors inform the choice of graduate institution that minority students make? Do these students take into account the reputation of the university or the department? Do they identify an area of specialization within which they want to concentrate and a person with whom they wish to work? Having such a faculty member as one’s sponsor significantly increases the probability that the student will earn a degree. Using inappropriate criteria to choose a graduate program could result in a mismatch between student and program, which, in turn, could result in extending the time to degree or dropping out. This information is needed to enhance the advice given to students to help them make informed choices. Among the questions that Dr. Brown suggests need further investigation are: • How do minority faculty view collegial relationships in PWCUs? • How do minority faculty view their ability to achieve their professional ambitions in PWCUs? I would extend the scope of both of these questions to include minority faculty at predominantly minority institutions because that is where more minority faculty are employed. As Dr. Brown has pointed out, we need to accomplish two things. First, we need more systematic analyses to determine whether differences in salary and promotion rates between minority men and minority women are associated with sex discrimination or cumulative factors that have little or nothing to do with gender. Second, we need more qualitative data about the experiences, perceptions, and attitudes of minority faculty in S/E departments.

References Calhoun, C., D. Light, and S. Keller. Sociology. 1994. 6th ed. New York: McGraw-Hill. Higginbotham, E., and L. Weber. 1992. Moving up with kin and community: Upward social mobility for black and white women. Gender and Society 6(3): 416–440. Leggon, C. B. 1987. Minority underrepresentation in science and engineering graduate education and careers. In Linda S. Dix, ed., Minorities: Their Underrepresentation and Career Differential in Science and Engineering. Washington, DC: National Academy Press. ——— . 1991. Graduate schools and careers. 1991. Paper presented at a workshop on Minorities, Science and Technology: An Agenda for Research and Action, Rensselaer Polytechnic Institute, Troy, NY. Leggon, C., and W. Pearson, Jr. 1994. The Baccalaureate Origins of African American Ph.D. Female Scientists. Paper presented to the Mid-South Sociological Association, Lafayette, LA. Leggon, C., and H. Etzkowitz. 1994. RBCUs. Paper presented at the annual meeting of the Graylyn Group, Tallahassee, FL. National Research Council. 1991. Summary Report 1990: Doctorate Recipients from United States Universities. Washington, DC: National Academy Press. ——— . 1993. Summary Report 1991: Doctorate Recipients from United States Universities. Washington, DC: National Academy Press.

The Preparation of Minorities for Academic Careers in Science and TheEngineering Next Stage 273 Pearson, W., Jr. 1986. Black Scientists, White Society, and Colorless Science: A Study of Universalism in American Science. Millwood, NY: Associated Faculty Press. ——— . African American Chemists. Manuscript. Sokoloff, N. J. 1992. Black Women and White Women in the Professions. New York: Routledge.

cora marrett

The Next Stage As a practitioner, it is clear to me that for our work in the future, parsimony is the watchword and boundary-crossing the approach. We need to target our efforts narrowly, using research to help us clear away the brush and identify the most significant pathways to a given end. We must learn the critical elements and then identify policies that are really going to address them. The same parsimonious approach applies to practice: given limits to human and financial resources, we can’t possibly afford to try everything. Parsimony in this case becomes the art of reducing complexity to the core elements we are able to handle. For example, trend analysis no longer yields many surprises. We are sufficiently convinced of the stability of trends to start asking the important questions. How do we account for the patterns that now appear to be so stable? The explanations can come from a number of arenas. In the quest for explanations, there are bodies of theory and ideas that are worth drawing on, and it will be necessary to cross the boundaries of individual disciplines to do so. In Vining-Brown’s discussion of faculty participation, she gets behind the trends, addressing not only what we know regarding participation but the correlates of that participation. She discusses the factors associated with participation in a way that should begin to give us clues about explanations. The challenge is to go anywhere, to draw on any discipline, that illuminates the issues. Random, uncoordinated efforts are not to our advantage; we need a level of collaboration beyond what we have seen in the past, along methodological lines and across fields of knowledge, to sort through what we really know and place remaining questions in some larger, as yet undefined, framework. We must collaborate also to reduce the burden on the people we are studying. One of the greatest problems at the National Science Foundation is that everybody wants information from and on institutions at the very time that they are cutting back on people who would supply that information. Unless we find ways to share and

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think through how we get our information, we are likely to find less and less useful data. A recent study of successful grantees from the National Science Foundation found that those who continue to get grants year after year are not repeating themselves. They have the same question but are looking at it from other possible angles, drawing on other disciplines and technologies. We must do the same. There are things to be gained from searching beyond our usual boundaries.

daryl e. chubin

Policy Perspectives

As a policy practitioner and commentator, I predictably will not join the chorus advocating more research. No doubt research is needed. But who are we attempting to convince through academic studies of the “pipeline” and those who aspire to a career in science? Indeed, reading between the lines of Shirley Vining Brown’s extensive references, we learn that not only have market forces failed to attract underrepresented groups to science (which we have all known for quite a while), but they have also failed to draw researchers to study the origins and prevailing conditions of this underrepresentation. It remains a situation for which we all bear some responsibility. We compound our ignorance through inaction. Fortunately, conferences can get the blood pumping faster, the personal rage swelling to outrage, and, upon taking stock, activate our collective resolve to do something more, better, faster, clearer. Just in case you doubt the need or find strategies elusive, I wish to share some policy thoughts that build on Brown’s and those of my colleague, Cora Marrett. We are in what Cheryl Leggon and Shirley Malcom (1994) have referred to (though not using this phrase) as a “post-equity” phase of interventions to increase the participation of minorities in science and engineering. Demographic realities bind us to a changing composition of students. The disparities between the presence of U.S. minority students and the inability of science and engineering faculty and institutions to prepare and deploy them for the national good is a growing embarrassment. We need “structural” remedies—some legislated, others federally funded by discretionary agency programs, and still others mandated and enforced locally. These remedies should be research-based, but residual empirical uncertainty is a constant in policy analysis and—like it or not—in policy making.

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In research terms, race, ethnicity, gender, and disability should have no predictive power when it comes to student preparation or achievement in science or engineering. If any of these characteristics do—which is surely the case, despite the empirical evidence Dr. Brown and others have specified—appropriate federal policy action for moral, economic, or other reasons is justified. For all her probing questions, however, Brown offers few specific policy options. She advises that federal and private agencies should provide “more funding for systematic research on the kinds of selection processes that influence minorities toward academic or non-academic careers,” as well as other studies to determine influences on career paths and directions. What, then, can be done in the policy arena? My preferences are as follows: More timely data. Most national datasets are two years old by the time researchers discover them. I cite as a case example the lack of unemployment and underemployment data on new Ph.D.s, even aggregated at the field level so that comparisons could be made and calls for “birth control” and academic downsizing evaluated (Tobias, Chubin, and Aylesworth 1995; Greene, Hardy, and Smith 1995–96). Our best compendium, looking across sectors and information sources, is the Commission of Professionals in Science and Technology. To produce policy-relevant analyses, researchers must wean themselves from the scholarly literature and seek data from published reports by government and professional societies, as well as unpublished reports. Otherwise minority issues will remain on the margins instead of becoming a mainstream research priority. Better institutional accountability to agency sponsors and potential student recruits on their retention and degree award rates, by field. We continue to specify our ignorance instead of reducing it because academic institutions, from graduate offices to department faculty, don’t want to document their failures by requiring exit interviews of their dropouts. NACME has reported in its newsletter retention rates for engineering undergraduates on an institution-by-institution basis. This places the burden where it belongs: on the local department and the environing campus. Students should not solely bear the stigma of attrition; it should be shared—better yet, prevented—by the institution that deemed the student capable of completing a degree. The institution, as Matyas and Malcom (1991) have argued and demonstrated, should do everything possible to make good on its investment. Incentives for expanding minority faculty through innovative predoctoral funding programs. The lack of minority faculty role models, as Brown has argued, requires that these incentives be present. Private foundations and federal agencies favor fellowship programs that are prestigious, portable, and directly fund students. I am personally inclined to favor traineeships; if structural change in the form and outcomes of graduate training is the goal, then directing institutions to produce new Ph.D.s equipped to pursue a range of career opportunities in science and engineering is possible through an award made to a specific training entity on campus. Let that entity distribute funds to and interact with students, and then monitor and report on their progress. Similarly, early-career programs for minority faculty could

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provide material support as well as a national support group (linked not just by the sporadic conference but electronically in an ongoing fashion to minority scientists and engineers practicing in nonacademic settings). Who says that role models are only found on college and university campuses? Targeted programs for minority students and professionals carry risks. As “set-asides,” they limit competition and can stigmatize recipients of the awards. This is a huge publicpolicy dilemma, as the Department of Justice review of agency affirmative-action programs, in the wake of the Supreme Court Adarand decision, is likely to reveal. The NACMEs, GEMs, and QEM Networks of the world (bless them) experience this tension. If the development of all human resources for science and engineering were a mainstream federal concern, as the Office of Technology Assessment (U.S. Congress 1991) and Chubin and Malcom (1996) have argued, there would be no need for setasides. In a less than perfect world, it is set-asides or nothing. So until we can reform and re-create the competitive research and student support mechanisms now used in all programs as human resources–friendly, set-asides will be a powerful policy tool. Put “field switching” in perspective. I may be concerned about losing capable science and engineering students to other fields for the wrong reasons (financial, campus climate), or fear their leaving school altogether, but I am unfazed by student indecision. Field switching is a form of shopping; the degree recipient, regardless of field, will contribute to the nation’s workforce in some way. So I distinguish between “bad” attrition and “good” attrition. We have become so fixated on the science and engineering pipeline that we downplay our commitments to the remaining 95 percent of students. How can we make them science “appreciative” if not “literate”? How can we teach them to teach themselves (acquire new skills) throughout adulthood? How can we view the science and engineering talent pool as potentially larger than the few early aspirants (the “called”) who can be identified, reinforced, prepared for courses, and propelled through the pipeline? Such a straight line from childhood aptitude to adult career and accomplishment should be viewed as the anomaly rather than the norm, that is, if we truly believe in individual differences, multiple intelligences, versatility, serendipity, effective mentors, and other cultural influences. I know I do (Chubin 1996). Modify federal policy about weighing tradeoffs and making choices. If the United States wants to diversify its science and engineering workforce, it must confront some thorny issues: (a) Recruitment of foreign nationals versus U.S. racial and ethnic minorities: no policy should disadvantage one group at the expense of another (Chubin and Tobias 1996). (b) Within the academic sector, the minority faculty presence is so slight that simultaneous examinations by field, tenure status, and gender are impossible: the NSF goal is the annual production of two thousand minority Ph.D.s in science and engineering by the year 2000. (We currently stand at less than half that total). Expanding the pool will require concerted action by universities, professional associations, sponsors, and employers in all sectors of the economy. (c) Mending the pipeline is meaningless if employing institutions maintain barriers or are unreceptive in more subtle ways to minority participation in science and engineering; universities should be penalized by withdrawing funding for practicing discrimination

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in the provision of opportunities for minority faculty and students, and they should be rewarded for diversifying their tenure-track faculty and student body. (Public policy, I’m afraid, is hopelessly behaviorist.) Early career preparation entails a series of critical transitions. Ensuring access to them and a readiness to succeed and advance to the next transition point is what policy interventions are all about. Brown’s findings have certainly stimulated thinking about interventions and how they might have enduring institutionalized impacts. References Chubin, D. E. 1996. Reculturing Science: Politics, Policy, and Promises to Keep. Science & Public Policy 23: 2–12. Chubin, D. E., and S. M. Malcom. 1996. Policies to Promote Women in Science. In C-S. Davis, A. B. Ginorio, C. S. Hollenshead, B. B. Lazarus, P. M. Rayman, et al., eds., The Equity Equation: Fostering The Advancement of Women in the Sciences, Mathematics, and Engineering, 1–28. San Francisco: Jossey-Bass. Chubin, D. E., and S. Tobias. 1996. Education and careers in science: Closing the gaps. Paper presented at the Annual meeting of the American Association for the Advancement of Science, Baltimore, MD. Greene, R. G., B. J. Hardy, and S. J. Smith. 1995–96. Graduate education: Adapting to current realities. Issues in Science & Technology (Winter): 59–66. Leggon, C. B., and S. M. Malcom. 1994. Human resources in science and engineering: Policy implications. In W. Pearson Jr. and A. Fechter, eds., Who Will Do Science? Educating the Next Generation 141–151. Baltimore, MD: Johns Hopkins University Press. Matyas, M. L., and S. M. Malcom, eds. 1991. Investing in Human Potential: Science and Engineering at the Crossroads. Washington, DC: AAAS. Tobias, S., D. E. Chubin, and K. Aylesworth. 1995. Rethinking Science as a Career: Perceptions and Realities in the Physical Sciences. Tucson, AZ: Research Corp. U.S. Congress, Office of Technology Assessment. 1991. Federally Funded Research: Decisions for a Decade. Washington, DC: U.S. Government Printing Office.

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EARLY CAREER PREPARATION

Industry

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terrence r. russell

Models for Studying Early Careers Minority Scientists and Engineers in Industry

Four Models for the Study of Science and Engineering Careers This chapter will explore our knowledge of the early career stages of scientists and engineers who are members of minority groups and how those careers are likely to develop. To do this adequately from a policy perspective, it is necessary to look back at formative developments, situations, and relationships and forward in terms of future goals, making predictions about the probability of connecting the present to the future. There are a number of approaches to the study of work and careers. The selection of models will determine the contents, scope, and utility of our efforts. In the case at hand, we would hope to affect decision making both by those who set policy in these areas (assuming anyone does so effectively) and the minority scientists and engineers who are making career and life choices. I will here consider four models or analogies. The usual model employed in studies of human resources in S/E is the well-known pipeline model, which is really a life table model, substituting survivorship in an S/E career for life survival. Attention is focused on percentages of survivors and probabilities of survival at various age and career stages, usually from sixth-grade science and math study through an imputed normative career end for the group in question, whatever that may be. When done comparatively, this type of analysis uses career life span as its main dependent variable. Comparisons are made by constructing life tables for various groups, necessarily defined according to fairly broad criteria such as gender or minority group status. The usual survival criterion in studies of S/E careers is continuation beyond the receipt of a doctorate in a salaried (or paid) S/E career in academia, industry, or government. When you drop (“drip” is probably more appropriate) out of such careers, you are no longer of interest to students of the pipeline. 281

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I am not convinced that this is a useful model for the analysis of the lifetime career; by focusing so narrowly on one-dimensional survivors, it does not allow full scope for a human capital approach to the study of careers. There are two vexing questions here: What is the connection between education and occupation/work? What should that connection be? With both a higher education system and labor market that are open to a great deal of individual choice, the linkage between education and work is, for most people, not as close as the pipeline model or a human capital approach based on the model would imply. In future considerations of this topic, we will want to use a richer model for those trained in S/E, richer in the range of possible career choices and outcomes and in our sense of how the process works. There are two other models or approaches to the study of careers that provide us with a better sense of the context of time-specific career outcomes as well as of lived careers. The status attainment model was developed by Otis Dudley Duncan and Arthur Goldberger in the 1960s. Unlike the pipeline model, a status attainment model applied to a group of scientists and engineers would treat S/E training and experience as independent variables, allowing for a wider variety of specified dependent variables (e.g., job status, salary) in a regression model of experiences, past statuses, and so forth. In brief, all of the human capital development that results in occupying a particular occupational strata could, in principle, be included in such a model. While status attainment career models resolve some of the problems of the pipeline model, they still depend upon the ability to quantify variables. Such models test or stimulate theoretical inference concerning the processes that link variables and the strength of those linkages. There needs to be an augmentation of the analytical process by which we can formulate theories of career development and make sound predictions about how the processes described in status attainment models actually work in terms of human interaction. Compared to the pipeline model, these models provide a richer description of the realities of careers, but they do have shortcomings. They depend on large-scale data sets for inclusion of a reasonable number of predictor variables. For similar reasons, actual models tend to get simplified by reducing the number of causal variables. Historically, simplifications have been accomplished by omitting women and minorities from the models. The natural history model was pioneered in the early years of this century by the Chicago School sociologists, whose dissertations and monographs included studies of such traditional occupations as waitress and such urban, exotic careers as taxi driver, dancer, jackroller, and hobo. This model owes much to biographical studies of the time (e.g., Lytton Strachey) but should not be confused with “social biography,” where the study of the life of one person is seen as a microcosm for an entire population. As Herbert Blumer has pointed out in his writing on the naturalistic method, the intent of such research is to find and describe stable and recurring patterns that characterize the group under study. The natural history model was adapted to the analysis of professional and protoprofessional careers in the 1950s. Howard Becker’s study of medical students, Sandford Dornbush’s study of career military men, Anselm Strauss and Lee Rainwater’s study of chemists, and C. Wright Mill’s study of white-collar professionals stand as examples. Natural history career studies have in common a focus on the career as an emergent phenomenon; they examine the interactions, experiences, and actions that

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create careers and the people who enter them. Such things as occupational role structure and role conflict, occupational and professional socialization, and the development of a professional identity are viewed as part of the opening, development, and closing of careers. In this model a career may or may not have an end goal (something that makes the career a success) either from the viewpoint of the person who is living the career or imputed by another individual. While the focus is shared, natural history career analysis has used a variety of data collection and analytical methods (including archival analysis), various interview strategies, ethnographic methods (characteristically, participant observation) and autobiographical methods, including the rarely (if ever) duplicated life-history analysis pioneered by Clifford Shaw in his 1930s study of the jackroller. Studies of women and minorities in S/E have utilized a variety of these methodological strategies, most notably Sally Hacker’s studies of women engineers, the British tradition of laboratory ethnographies, and the depth-interview methods used by Willie Pearson in his study of African American chemists. My preferred approach for both policy study and individual decision-making purposes is a mid-range analytical strategy that yields information useful for both models, a dialectical model that uses the insights from a natural history of careers to construct indicators and metrics useful to a status attainment model, both formal and informal. The results of the status attainment model are then used to drive an increasingly sophisticated natural history analysis of careers. I favor what I have called the pinball model, which allows for emergence, chance, interactive effects, and “threedimensional movement” in careers. (The reader is free to substitute another randomprocess term for “pinball.”) The pinball model makes explicit the reflexivity of the human-capital model, where (in career models) what counts as “capital” is determined by utility concerning career outcomes. By dealing with relevant interaction, the model can provide insight into processes where human capital is formed, accumulated, and revalued (up or down). The pinball model is preferable to the pipeline model when studying those individuals who choose industrial S/E careers; industrial-career progression is soon characterized by a movement away from research and the practice of S/E and into the management of large combinations of resources, of which S/E is only a small part. In particular, the pipeline model leads to an ironic conclusion: successful industrial scientists and engineers are not, for the most part, survivors in the S/E pipeline but have increased their impact on S/E. It should be clear from my choice of analogies that I do not propose a model that emphasizes the power of individual choice. Bouncing off bumpers and falling into immutable final slots is a random process with highly structured outcomes. Little depends on the desires of the steel ball as it makes its way through the game. This is not to say that every ball rolling through the game has the same probabilities of hitting the highest-scoring bumpers or landing in a given slot. Sometimes one can tilt the machine to ensure high or low final scores (usually the former, but the latter case is useful for my analogy) and leave the ordinary run of probabilities to ordinary balls. Not surprisingly, this suggests a descriptive basis for the stratification of job placements upon graduation: stratification is the strength of coupling in career paths, measured by the probability that a particular type of training or education (e.g., an engineering degree) will lead to a particular type of job. The coupling can be strong

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or weak. Although negative relationships exist (by virtue of recruiting for training rather than skills), this is not a major issue in the case at hand. This probabilistic aspect of the pinball model leads to several interesting questions: What differences exist in probability patterns across occupational groups? Across racial and ethnic groups within occupations? What is the rate of change in these patterns and whom does it affect and why? (e.g., general economic situation). Related to these questions is the issue of the expectation of students, who usually exhibit a faith in the strongcoupling hypothesis (viz. quality work in school results in a quality first-job placement) that numerous studies in the “locus of control” literature have proven to be somewhat misplaced. I suggest that whether the relationship is strong or essentially random depends, first of all, on the strength of demand in a particular occupation. In good times students attribute their success to their own efforts, whereas in times of low demand, when who ends up where is a much more random process, they attribute their lack of success to external forces. The point is that there is little control in either case. Patterns of difference also exist across various subgroups within training and occupational groups; a situation where one subgroup experiences a strong link between training and first job and another finds a more random process at work is a definition of a stratified job market. The clearer the pattern, the more rigid the stratification. For minority scientists and engineers the crucial questions are: Do differences of education-to-occupation predictability exist across racial and ethnic groups? If the job-attainment process seems to be random, is this true for everyone in the same training cohort and for members of the same minority group within the cohort? This emphasis on comparative education-to-occupation predictability gives us a finer measure of the quality of intergroup relations than categorial concepts such as exclusion. To use these concepts opens the way to a new set of questions that will get at the experiences of education-to-occupation mobility, especially as experienced by minority scientists and engineers. Contemporary policy studies of minority scientists and engineers in industry use a variety of analytical tactics to define comparison groups in order to establish differential career life tables. As a general comparative strategy, it is first useful to study the careers and conditions for success for scientists and engineers generally, and then to demonstrate that these conditions and their availability are either the same or different for minority scientists and engineers, recognizing that the patterns of experience of various minority groups may differ. It is precisely these patterns of minority S/E experience that I wish to posit as a set of research initiatives. I submit that we know little about this very important yet largely unexamined analytical resource. Driven by the pipeline model and its variants (e.g., maturity-curve salary studies) or, to a lesser extent, based on status attainment models, national data sets exist that describe minority access and participation in S/E industries—or lack of it. Data sets available from NCES, NSF/NRC, and professional societies (e.g., ACS, AAES, AIP, NSPE, and sporadically AIChE) address S/E issues on a national basis, including minority S/E issues. While this is a large-scale effort, it is still difficult to answer some pressing policy questions because of such basic problems as sparse populations, the difficulty of phrasing and obtaining data on productivity measures, and the uncertainties of gathering data from individuals that would best be obtained from em-

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ployers (e.g., fringe benefits and the availability of various opportunities and programs in the workplace), and vice versa. In studying minority scientists and engineers in industry, private employers have not come forward to provide comparative detailed data. As the pinball model illustrates, what is missing from studies using these data sets is analysis of the human activity that generates quantified patterns of outcomes. Since the analysis is undone, there remains an unexamined resource for explaining the abstract patterns described by statistical analysis. This is a policy resource as well. In studying careers, we want to know something of the cumulative conditions, experiences, actions, choices, and attitudes that result in quantitative patterns that describe the career outcomes of a whole nation or, in the case at hand, a whole occupational group. If national patterns are of interest, it is presumably because someone wants to change them. To do so effectively, we must utilize all the resources we can muster and pray that when we do act on the basis of our analysis, we know enough and are smart enough to do no harm. The balance of this chapter presents key questions concerning those policy issues surrounding the study of the careers of minority scientists and engineers in industry that could be addressed using the models and methods discussed.

What We Need to Know Human-Capital Development and Professional Socialization 1. What resources (e.g., human capital) do individuals bring to a job search and their early careers that are correlated with success and promotion? Examples include earned degrees, citizenship status, experience, specialized skills, a record of productivity, and professional flexibility and mobility. 2. Where do people develop these resources and how accessible are development activities? How is access stratified? 3. How is a sense of professional identity developed? What relationships and organizations are involved? How does “assuming the role of the other” work in the professional mentoring relationship and how great an impact does it have on professional identity? 4. For individuals, how salient is the S/E role and what role conflicts does it entail? Are these conflicts different in nature or intensity for minority scientists and engineers? Are there differences across minority groups? What about gender differences? Unexamined Resources: Experience Before Graduation 1. How are decisions made in terms of going to college, persisting to a degree, and staying for graduate school? 2. Where does one obtain assistance and information useful for getting placed and supported at graduate and postdoctoral levels?

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3. For minority employees in particular, what types of early (precollege) identification and support programs are operated by corporations? 4. How effective is summer work experience in creating successful S/E employees, and at what points in the student’s career is it available? How extensive are these programs? 5. How effective are academia/industry cooperative programs in creating successful S/E employees? Entry into a Career: The Academia/Industry Transition 1. How do students actually find industry jobs and how much of the process is organized by institutions and hiring companies? 2. How does the “off-the-books” job-finding process work? 3. Who are the “interface players” that get students through the transition from academia to industry and what resources do they make available to the student? 4. How are industry opportunities stratified by type of institution or department? What is the level of recruitment programming targeted for minority S/Es? The Role of Faculty and Other Advisory Staff 1. What do faculty and other advisory staff know about industry recruiting and hiring (especially for minorities) and how do they facilitate the school/ work transition? 2. What are faculty resources and responsibilities and how are the latter articulated in terms of faculty role descriptions and workloads? 3. How do faculty members communicate future job expectations and jobsearch skills—if they do so at all? The Role of Academic Institutions or Departments 1. What types of resources do academic institutions or departments allocate to the academia/industry transition? 2. How effective and user-friendly is the career placement effort in S/E? 3. Apart from the interviewing process, what types of resources exist for students? 4. What search support mechanism exists for less than straight-A students who will not be interviewed by the “top ten” recruiters? The Role of Corporations and Corporate Life: Recruiting Practices 1. At which institutions and departments do corporations recruit and why don’t they do so at others? 2. What resources in terms of career support, training, and guidance do corporations allocate to aiding new minority S/E employees and helping them to establish their careers? What about S/E employees in general?

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3. To what extent do business conditions differentially affect minority recruitment and employment, that is, what happens to minority scientists and engineers when recruitment and staffing occurs at minimal or perhaps less than replacement levels? 4. How friendly to minority scientists and engineers is the corporate culture and informal interaction in the workplace? Are some industries and corporations friendlier than others and, if so, why? The Role of Corporations and Corporate Life: Launching a Career 1. What are good career “side bets” in industrial S/E? Are they open to minorities? 2. Does the firm maintain dual promotion, career, and salary ladders for S/E and managerial staff? Are they really equal? 3. How effectively do multinational corporations prepare their employees —especially minority scientists and engineers—for success in a multinational business world? 4. What formal and informal continuing education opportunities exist? How closely are they linked to advancement and promotion? Is there a “sabbatical” program? 5. Who participates in industrial postdoctoral programs? Are they good for your career? 6. What is the level of minority participation in career-broadening assignments, such as geographic rotations in multinational corporations? 7. Is there a differential impact on minority scientists and engineers when a corporation decides it’s time to downsize? The Role of Professional and Scientific Associations 1. What programs, resources, and information do professional and scientific associations have in place to assist in the school-to-work transition and early career success? Do these programs work? 2. Which associations are actively involved? Certainly the list should include AAAS, AIP, ACS, AAES, and NSPE. It should also include organizations devoted to minority S/E careers generally, like NACME, or the careers of members of particular racial or ethnic groups, like NOPABCChE, the Chinese-American Chemical Society, the India Chemist’s Club, the Hispanic Science and Engineering Society, and AISES.

Selected References ACS Corporation Associates. 1993. Diversity in the Chemical Workforce of the 21st Century: Building a Competitive Advantage (annual symposium). Washington, D.C.: American Chemical Society.

288 Early Career Preparation: Industry Adelman, Clifford. 1995. Lessons of a Generation: Education and Work in the Lives of the High School Class of 1971. San Francisco, Ca.: Jossey-Bass. Alcohol, Drug Abuse, and Mental Health Administration. 1990. Guidelines for Supplements for Underrepresented Minorities in Biomedical and Behavioral Research Supported by ADAMHA. Dept. of Health and Human Services, Public Health Service. Washington, DC: U.S. Government Printing Office. American Chemical Society. 1990. Starting Salaries of Chemists and Chemical Engineers: Analysis of the American Chemical Society’s Survey of Graduates in Chemistry and Chemical Engineering. Washington, D.C.: American Chemical Society. ——— . 1992. Salaries 1992. Analysis of the American Chemical Society’s 1992 Survey of Salaries and Employment. Washington, D.C.: American Chemical Society. ——— . 1994. Current Trends in Chemical Technology, Business, and Employment. Washington, D.C.: American Chemical Society. American Chemical Society, Committee on Professional Training (ACS–CPT). 1990. ACS Committee on Professional Training: 1989 Annual Report. Chemical Engineering News 68: 29–35. Anderson, M. S., and K. S. Louis. 1994. The graduate student experience and subscription to the norms of science. Research in Higher Education 35(3): 273–299. Anon. 1992. Minorities in science: A directory. Science 258(5085): 1235. Anon. 1992. Minorities in science: Bibliographic highlights. Science 258(5085): 1170. Anon. 1992. Data Points II: Workplace. Science 258(5085): 1199. Arbona, C. 1990. Career counseling research and Hispanics: A review of the literature. Counseling Psychologist 18(2): 300–323. Arnold, John, and Nigel Nicholson. 1991. Construing of self and others at work in the early years of corporate careers. Journal of Organizational Behavior 12(7): 621–639. Atkinson, D. R., A. Casas, and H. Neville. 1994. Ethnic-minority psychologists: Whom they mentor and benefits they derive from the process. Journal of Multicultural Counseling and Development 22(1): 37–48. Austin, Gail T. 1993. African-American Women in the Sciences and Related Disciplines. Washington, D.C.: Science Reference Section, Science and Technology Division, Library of Congress. Bahniuk, M. H., J. Bobos, and S. E. K. Hill. 1990. The impact of mentoring, collegial support, and information adequacy on career success: A replication. Journal of Social Behavior and Personality 5(4): 431–451. Bailyn, L. 1987. Experiencing technical work: A comparison of male and female engineers. Human Relations 40(5): 299–312. Baum, Eleanor. 1989. The Cooper Union 1989 National Survey of Women Engineers. New York: Albert Nerken School of Engineering of the Cooper Union for the Advancement of Science and Art. Berryman, Sue E. 1983. Who Will Do Science? Trends and Their Causes in Minority and Female Representation Among Holders of Advanced Degrees in Science and Mathematics. New York: Rockefeller Foundation. ——— . 1985. Integrating the sciences. New Perspectives 17: 16–22. Blackwell, James E. 1981. Mainstreaming Outsiders: The Production of Black Professionals. New York: General Hall. ———. 1989. Mentoring: An action strategy for increasing minority faculty. Academic Bulletin of the AAUP 75(5): 8–14. ——— . 1992. Minorities in the liberation of the ASA? American Sociologist 23(1): 11–17. Blockstein, David E. 1990. Women and minorities: How are the AIBS societies doing? Bioscience 40(8): 607.

Models for Studying Early Careers: Minority Scientists and Engineers in Industry

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Brautigan, D. L. 1994. Incorporating minorities in science. Science 263(5147): 593–594. Brennan, Mairin B. 1993. Programs seek to aid movement of minorities into science. Chemical and Engineering News 71(46): 59. Brickhouse, N. 1994. Bringing in outsiders: Reshaping the sciences of the future. Journal of Curriculum Studies 26(4): 401–416. Bureau of Labor Statistics. 1985. How Workers Get Their Training (Bulletin 2226). Washington, D.C.: U.S. Government Printing Office. Cameron, T. 1994. Incorporating minorities in science. Science 263(5147): 595. Campbell, George, Jr. 1992. The Gender Gap in Minority Engineering Education. A Research Letter of the National Action Council for Minorities in Engineering 3(1): 1–8. Carey, Phillip. 1977. Engineering education and the black community: A case for concern. Journal of Negro Education 46(1): 39–45. Chan, Sucheng. 1989. Beyond affirmative action: Empowering Asian American faculty. Change 21(6): 48–51. Chipman, S. F., and V. G. Thomas. 1987. The participation of women and minorities in mathematical, scientific, and technical fields. Review of Research in Education 14: 387– 430. Cole, Jonathan, R. 1979. Fair Science: Women in the Scientific Community. New York: Free Press. Cole, J. R., and S. Cole. 1973. Social Stratification in Science. Chicago: University of Chicago Press. College Placement Council. 1990. College Recruiting and Relations Survey, 1990. Bethlehem, Pa.: College Placement Council. Commission on Professionals in Science and Technology. 1985. The International Flow of Scientific Talent: Data, Policies and Issues. Commission on Professionals in Science and Technology. ——— . 1990. Salaries of Scientists, Engineers, and Technicians: A Summary of Salary Surveys. 14th ed. Commission on Professionals in Science and Technology. Committee on Biomedical and Behavioral Research Personnel. 1989. Biomedical and Behavioral Research Scientists: Their Training and Supply. Washington D.C.: National Academy Press. Congrove, Stephen F., and Steven Box. 1970. Science, Industry and Society: Studies in Sociology of Science. New York: Barnes and Noble Press. Cooper, S. E., and D. A. G. Robinson. 1987. A comparison of career, home, and leisure values of male and female students in engineering and the sciences. Journal of College Student Development 28(1): 66–70. Culotto, Elizabeth. 1992. Black colleges cultivate scientists. Science 258(5085): 1216. Culotto, Elizabeth, and Karen Fox. 1993. Trying to change the face of science. Science 262 (5136): 1089. Dawkins, M. P. 1989. The persistence of plans for professional careers among blacks in early adulthood. Journal of Negro Education 58(2): 220–231. Dix, Linda S., ed. 1987. Minorities: Their Underrepresentation and Career Differentials in Science and Engineering. Proceedings of a Workshop. Office of Scientific and Engineering Personnel, National Research Council. Washington, D.C.: National Academy Press. Evetts, Julia. 1994. Women and careers in engineering: Continuity and change in the organisation. Work, Employment and Society 8(1): 101–112. Falk, Charles, 1989. Foreign engineers and engineering students in the United States. In Foreign and Foreign-Born Engineers in the United States. Washington, D.C.: National Academy Press.

290 Early Career Preparation: Industry Fox, Karen. 1993. A guide to minority aid from scientific societies. Science 262 (5136): 1134. ——— . 1993. Minority networks forge bonds in chemistry. Science 262(5136): 1126. Gaskill, L. R. 1993. A conceptual framework for the development, implementation, and evaluation of formal mentoring programs. Journal of Career Development 20(2): 147–160. Gaston, J., and Willie Pearson Jr. 1986. The social status of contemporary black chemists. Paper presented at the American Chemical Society’s Symposium on the Status of the Chemist in 1985, New York City. Gibbons, Ann. 1993. White men can mentor: Help from the majority. Science 262(5136): 1130. Glass, Karen, ed. 1990. Realizing the Potential of Women and Minorities in Engineering: Four Perspectives from the Field. Washington, D.C.: National Governors Association. Haberfeld, Y., and Y. Shenhav. 1990. Are women and blacks closing the gap? Salary discrimination in American science during the 1970s and 1980s. Industrial and Labor Relations Review 44(1): 68–82. Harding, Sandra, ed. 1993. The “Radical” Economy of Sciences: Toward a Democratic Future. Bloomington: Indiana University Press. Hecker, Daniel E. 1992. Reconciling conflicting data on jobs for college graduates. Monthly Labor Review (July): 3–12. Heininger, S. Allen. 1993. Minorities in the chemical sciences. Chemical & Engineering News 71(5): 40. Heller, R. S., and C. D. Martin. 1994. Attracting young minority women to engineering and science: Necessary characteristics for exemplary programs. IEEE Transactions on Education 37(1): 8–12. Heylin, Michael. 1992. Job market for chemists weakens, salaries hold up quite well. Chemical & Engineering News 13(July): 38–46. ———. 1993. ACS makes plans to attract more minorities into chemistry. Chemical & Engineering News 71(41): 40. Hill, O. W., and W. C. Pettus. 1990. Three studies of factors affecting the attitudes of blacks and females toward the pursuit of science and science-related careers. Journal of Research in Science Teaching 27(4): 289–314. Holden, Constance. 1986. Prospects discouraging for minorities in science. Science 234: 1316. ——— . 1992. Minority survivors tell their tales. Science 258(5085): 1204. Holden, Constance, and Ann Gibbons. 1993. Are foreigners squeezing minorities out? Science 262(5136): 1109. Holsinger, Donald B., and Roberto M. Fernandez. 1987. School to work transition profiles: Mexican American and non-Hispanic white high school graduates. Sociology and Social Research 71(3): 211–215. Hoyte, R. M. 1994. Incorporating minorities in science. Science 263 (5147): 594–595. Humphreys, Sheila, ed. 1982. Women and Minorities in Science: Strategies for Increasing Participation. Boulder, Colo.: Westview Press for the American Association for the Advancement of Science. Institute for Science, Space, and Technology. 1990. Strengthening American Science and Technology: The Role of Minorities. Baltimore, Md.: Career Communications Group. Jackson, W. M. 1984. Black colleges and universities: National resource for black science and engineering manpower. In Black Colleges and Universities: Challenges for the Future, ed. A. Garibaldi. New York: Praeger. Jagacinski, C. M. 1987. Engineering careers: Women in a male-dominated field. Psychology of Women Quarterly 11(1): 97–110. Jagacinski, C. M., W. K. Lebold, and K. W. Linden. 1987. The relative career advancement of men and women engineers in the United States. Work and Stress 1(3): 235–247.

Models for Studying Early Careers: Minority Scientists and Engineers in Industry

291

Johnson, I. H. 1988. Turning minority students on to science careers. Bulletin of Science Technology and Society 8(6): 608–611. Jones, M. G., and J. Wheatley. 1988. Factors influencing the entry of women into science and related fields. Science Education 72(2): 127–142. Katz, M. R. 1988. Computerized guidance and the structure of occupational information. Prospects 18(4): 515–525. Kellough, J. Edward, and Euel Elliott. 1992. Demographic and organizational influences on racial/ethnic and gender interaction in Federal Agencies. Social Science Quarterly 73(1): 1–11. Kidd, Charles V. 1986. International mobility of bioscientists: Trends and perceptions, country by country. Perspectives in Biology and Medicine 29(2): S21–S33. Koelewijn-Strattner, Gijsberta Johanna. 1990. Race, gender and the scientific professions: Double negative or double jeopardy? Master’s thesis, 1990. Landefield, T. D. 1994. Incorporating minorities in science. Science 263(5147): 595. La-Noue, George R., and Nancy Lloyd-Miller. 1976. Professional societies and equal employment. Trans-Action 13: 52–58. Leggon, Cheryl B. 1980. Black female professionals: Dilemmas and contradictions of status. The Black Woman, ed. LaFrance Rodgers-Rose. Beverly Hills, Ca.: Sage Publications. ——— . 1987. Minority underrepresentation in science and engineering graduate education and careers: A critique. In Minorities: Their Underrepresentation and Career Differentials in Science and Engineering, Proceedings of a Workshop, ed. Linda S. Dix, Office of Scientific and Engineering Personnel, National Research Council. Washington, D.C.: National Academy Press. ——— . 1991. Graduate schools and careers. Paper presented at a Renesselaer Polytechnic Institute workshop on Minorities, Science, and Technology: An Agenda for Research and Action, Troy, N.Y. Lenfant, C. 1993. NHLBI programs for minority researchers: A life in science. Circulation 88(6): 2488. Levidow, Les. 1978. Grunswick: Technology and class struggle. Radical Science Journal 6– 7: 119–128. Levinger, George, et al. 1987. Black employment opportunities: Macro and micro perspectives. Journal of Social Issues 43(1): 1–78. Luzzo, D. A. 1993. Career decision-making differences between traditional and nontraditional college students. Journal of Career Development 20(2): 113–120. McCorquodale, Patricia. 1984. Self-image, science, and math: Does the image of the “scientist” keep girls and minorities from pursuing science and math? Paper presented at the 79th meeting of the American Sociological Association. McDonald, Jean, Marianne Clark, and Eric Dobson. 1990. Increasing the Supply of Women and Minority Engineers: An Agenda for State Action. Washington D.C.: National Governors Association. Macias, J. 1993. Forgotten history: Educational and social antecedents of high achievement among Asian immigrants in the United States. Circulation Inquiry 23(4): 409–432. Mahaney, F. X. 1994. Minorities remain underrepresented in science and medicine. Journal of the National Cancer Institute 86(2): 83–84. Malcom, Shirley M. 1981. Women/minorities in science and technology. Science 214 (4517): 137. ———. 1985. The place of minorities in the scientific and technical workforce. Black Scholar 5: 50–55. Massey, Walter E. 1992. A success story amid decades of disappointment. Science 258(5085): 1177.

292 Early Career Preparation: Industry Matthew, Christina. 1990. Underrepresented Minorities and Women in Science, Mathematics, and Engineering: Problems and Issues for the 1990s. Washington, D.C.: Library of Congress. Matyas, Marsha Lakes, and Shirley M. Malcom, eds. 1991. Investing in Human Potential: Science and Engineering at the Crossroads (No. 91–39S). Washington, D.C.: American Association for the Advancement of Science. Melnick, Vijaya L., and Franklin D. Hamilton, eds. 1977. Minorities in Science: The Challenge for Change in Biomedicine. New York: Plenum. Middlecamp, C. H., and J. W. Moore. 1994. Race and ethnicity in the teaching of chemistry: A new graduate seminar. Journal of Chemical Education 71(4): 288–290. Miller, Susan Katz. 1993. Minorities move from lab rats to policy workers. Science 262(5136): 1101. Mortimer, J. T., and M. D. Finch. 1986. The development of self-esteem in the early work career. Work and Occupations 13(2): 217–239. National Institute of Diabetes and Digestive and Kidney Diseases. 1991. Minority Research Training Opportunities and Programs: National Institute of Diabetes and Digestive and Kidney Disorders (NIH Pub: 91–3181). Bethesda, Md.: U.S. Dept. of Health and Human Services, Public Health Services, National Institute of Health. National Research Council. 1988. Foreign and Foreign-Born Engineers in the United States: Infusing Talent, Raising Issues. Washington, D.C.: National Academy Press. ——— . 1989. Biomedical and Behavioral Research Scientists: Their Training and Supply. 3 vols. Washington, D.C.: National Academy Press. ———. 1993. 1991 Survey of Doctorate Recipients Longitudinal File. Office of Scientific and Engineering Personnel. Washington, D.C.: National Research Council. ——— . 1993. Control Counts for Doctoral Scientists and Engineers in the U.S. in 1989 and 1991. Office of Scientific and Engineering Personnel. Washington, D.C.: National Research Council. National Science Foundation. 1982. Women and Minorities in Science and Engineering. Washington, D.C.: U.S. Government Printing Office. ——— . 1984. Women and Minorities in Science and Engineering. Washington, D.C.: U.S. Government Printing Office. ———. 1985. A Directory of Federal R&D Agencies in Science and Engineering Programs for Women, Minorities, and Physically Handicapped Persons (NSF 85–51). Washington, D.C.: National Science Foundation. ———. 1987. Characteristics of Doctoral Scientists and Engineers: Detailed Statistical Tables (NSF 88–331b). Washington, D.C.: National Science Foundation. ——— . 1987. Foreign Citizens in U.S. Science and Engineering: History, Status and Outlook. Washington, D.C.: National Science Foundation. ——— . 1988. Legacy to Tomorrow (NSF–88–49). Directorate for Science and Engineering Education, Division of Research Career Development, Minority Graduate Fellowship Program. Washington, D.C.: National Science Foundation. ——— . 1990. Women and Minorities in Science and Engineering. Washington, D.C.: U.S. Government Printing Office. ——— . 1992. Women and Minorities in Science: An Update. Washington, D.C.: U.S. Government Printing Office. Nelson, F. Howard. 1985. Black computer workers: Closing the gap in high-technology employment. Journal of Negro Education 54(4): 548–557. Nettles, M. T. 1990. Success in doctoral programs: Experiences of minority and white students. American Journal of Education 98(4): 494–522. Oakes, Jeannie. 1990a. Lost Talent: The Underparticipation of Women, Minorities and Disabled Persons in Science. Santa Monica, Ca.: Rand Corporation.

Models for Studying Early Careers: Minority Scientists and Engineers in Industry

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———. 1990b. Opportunities, achievement, and choice: Women and minorities. Studies in science and mathematics. Review of Research in Education 16: 153–222. Ortiz, Roxanne Dunbar. 1981. Developing Indian academic professionals. Integrateducation 19: 38–41. Pearson, Willie, Jr. 1960. Black American participation in American science: Winning some battles but losing the war. Journal of Educational Equity and Leadership 6: 45–59. ———. 1985. Black Scientists, White Society and Colorless Science: A Study of Universalism in American Science. Port Washington, N.Y.: Associated Faculty Press. ———. 1987. The graduate education and careers of underrepresented minorities in science and engineering. In Minorities: Their Underrepresentation and Career Differentials in Science and Engineering, Proceedings of a Workshop, ed. Linda S. Dix, Office of Scientific and Engineering Personnel, National Research Council. Washington, D.C.: National Academy Press. Pearson, Willie, Jr., and J. Gaston. 1985. Race and universalism in American science. Paper presented at a meeting of the American Association for the Advancement of Science, Los Angeles, March 1985. Pearson, Willie, Jr., and Alan Fechter, eds. 1994. Who Will Do Science? Educating the Next Generation. Baltimore, Md.: Johns Hopkins University Press. Penley, E. S., S. Gould, L. Delavina, and K. Murphy. 1989. An early career focused study of Hispanic American college graduates in business. Hispanic Journal of Behavioral Sciences 11(4): 366–380. Pool, Robert. 1990. Who will do science in the 1990s? Science 248(4954): 433. Rawls, Rebecca L. 1991. Minorities in science: Growing effort aims to increase participation of historically underrepresented groups. Chemical & Engineering News 69(15): 20. Reed, Rodney J. 1978. Increasing the opportunities for black students in higher education. Journal of Negro Education 47(2): 143–150. Robinson, D. A. G. 1994. Career exploration with freshman engineering students. Journal of College Student Development 35(1): 68–69. Rudolph, H. 1989. Women engineers in the FRG: Lonely pioneers forever. Social Science Information 28(2): 345–366. Sankaran, N. 1994. Report: Gender, ethnic diversity coming slowly to science. Scientist 8(5): 3. Scheetz, L. Patrick. 1991. Recruiting Trends, 1991–92: A Study of Businesses, Industries, and Governmental Agencies Employing New College Graduates. East Lansing: Michigan State University. Scott, N. E. 1989. Differences in mentor relationships of non white and white female professionals and organizational mobility: A review of the literature. Psychology 26(2–3): 23–26. Shenhav, Y. A., and Y. Haberfeld. 1988. Scientists in organizations: Discrimination processes in an internal labor market. Sociological Quarterly 29(3): 451–462. Smith, E. J. 1983. Issues in racial minorities’ career behavior. In Handbook of Vocational Psychology, ed. W. B. Walsh and S. H. Osipow. Hillsdale, N.J.: Lawrence Erlbaum. ———. 1991. The making of black scientists. Black Issues in Higher Education 14 (March): 68. Smith, Earl, and Willie Pearson Jr. 1989. Scientific productivity among a sample of black and white female Ph.D.s. Journal of Social and Behavioral Sciences 35: 153–74. Stith, James H. 1990. Guest comment: Endangered species, the black physicist. American Journal of Physics 58(5): 427. Tang, Joyce. 1991. The career mobility of Asian American engineers: Earnings, career status, promotions, and attrition. Ph.D. diss., University of Pennsylvania.

294 Early Career Preparation: Industry ———. 1993a. Caucasians and Asians in engineering: A study in occupational mobility and departure. in Research in the Sociology of Organizations, ed. S. B. Bacharach, 217–256. Greenwich, Conn.: JAI Press. ——— . 1993b. Whites, Asians, and blacks in science and engineering: A reconsideration of their economic prospects. Research in Social Stratification and Mobility 12: 249–291. Task Force on Women, Minorities, and the Handicapped in Science and Technology. 1988. Changing America: The New Face of Science and Engineering: Interim Report. Washington, D.C.: Task Force on Women, Minorities, and the Handicapped in Science and Technology. ———. 1989. Changing America: The New Face of Science and Engineering: Final Report. Washingon, D.C.: Task Force on Women, Minorities, and the Handicapped in Science and Technology. Thomas, Gail E. 1986. The access and success of blacks and Hispanics in U.S. graduate and professional education: A Working Paper. Washington, D.C.: National Academy Press. ———. 1992. Participation and degree attainment of African American and Latino students in graduate education relative to other racial and ethnic groups: An update from Office of Civil Rights Data. Harvard Educational Review 62(1): 45–65. Tomlinsonclarke, S. 1994. A longitudinal study of the relationship between academic comfort, occupational orientation and persistence among African American, Hispanic and white college students. Journal of College Student Development 35(1): 25–28. Tun, S. 1986. Chinese in the U.S. Chemical Profession: 1985 Statistical Profile. Washington, D.C.: American Chemical Society. U.S. House. 1991. Committee on Science, Space, and Technology. Traditional and Nontraditional Sources of Future Research Scientists: Hearing Before the Subcommittee on Science, Space, and Technology. 102nd Cong. 1st sess, 11 July. Washington, D.C.: U.S. Government Printing Office. Vetter, Betty M. 1984. Professional Women and Minorities: A Manpower Data Resource Service. Washington, D.C.: Scientific Manpower Commission. ——— . 1989. American Minorities in Science and Engineering (Occasional Paper 89–3). Washington, D.C.: Commission of Professionals in Science and Technology. ——— . 1989. Replacing Science and Engineering Faculty in the 1990s (Occasional Paper 89–4). Washington, D.C.: Commission of Professionals in Science and Technology. ———. 1990. Women in Science and Engineering. An Illustrated Progress Report (Occasional Paper 90–4). Washington, D.C.: Commission on Professionals in Science and Technology. ——— . 1992. Supply and Demand in Science and Engineering (Occasional Paper 91–4). Washington, D.C.: Commission on Professionals in Science and Technology. Walsh, John. 1987. Spotlight brighter on minorities in science: NSF, Black Caucus initiatives reflect growing concern about access to research careers. Science 238: 265. Wilkinson, Doris, 1978. A profile: Minorities in sociology and other behavioral sciences. American Sociological Association Footnotes 6(8): 6–8. Wilkinson, R. Keith. 1990. Science and Engineering Personnel: A National Overview (Special Report NSF 90–310). Washington, D.C.: National Science Foundation. Wright, Cheryl A., and Scott D. Wright. 1987. The role of mentors in the career development of young professionals. Family Relations 36(2): 204–208. Wyche, James H., and Henry T. Frierson Jr. 1990. Minorities at majority institutions. Science 249(4972): 989–991. Xie-Yu. 1990. The process of becoming a scientist. Ph.D. diss., University of Michigan, Ann Arbor.

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Young, H. A., and B. A. Young. 1974. Scientists in the Black Perspective. Louisville, Ky.: Lincoln Foundation. Zanetti, R. 1994. Minority hiring a business imperative. Chemical Engineering 101(12): 5. Zinn, Maxine-Baca. 1982. Mexican-American women in the social sciences. Signs 8(2): 259– 272.

henry etzkowitz

Why Are Minority and Women Scientists Still Treated So Badly? Despite the growing literature on the problems of minorities and women in science, important issues remain to be addressed (Pearson and Fechter 1993). There is an emerging consensus that the experiences of minorities and women must be examined in the organizational and cultural context of academic and industrial environments as well as through the prism of individual experience (Russell, this volume). The intersection of the individual and the organization is best captured qualitatively through in-depth interviews and focus groups; only then can precise indicators be constructed to measure incidence quantitatively through surveys and secondary analysis of available data sets. Such a research program is difficult to mount under the constraints of the project grant system; even when multiple methods are used, time pressures virtually mandate simultaneity. A strong case can also be made for supporting a program of longitudinal investigation of academic and industrial careers and organizational change. Such a study of Ph.D. programs in the sciences and engineering would broaden and deepen our knowledge of the factors that contribute to or retard the progress of minorities and women in graduate programs. A parallel investigation of corporate and governmental laboratories should be undertaken to access the experience of minorities and women at different stages of the career ladder. Preparatory to mounting such largescale projects (Etzkowitz and Mulkey 1991; Etzkowitz, Kemelgor, Neuschatz, Uzzi, Pearson, and Leggon 1994), opportunities exist to extend the time frame and go back into the field with samples from recent projects to obtain at least the beginnings of longitudinal qualitative data (Etzkowitz and Kemelgor 1994; Etzkowitz and Fox 1992). Finally, European and Canadian models for creating networks of researchers

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and research centers to carry out coordinated projects should be considered for U.S. adaptation (Etzkowitz 1994). An event such as the NACME conference is all too unusual; the typical U.S. experience in the social sciences is to meet well down the research road, after data collection has been completed, rather than at the project formulation stage.

Background Analysis of the recruitment and promotion of minorities and women and the career paths they follow has important implications for industrial, academic, and governmental policies for human resources development in science. In June 1990, a workshop entitled “Toward a Gender-Free Paradigm for Computer Science,” sponsored by IBM, was held at the meetings of the National Society of Computer Education in Nashville. The convenor of the workshop, Professor Dianne Martin of the EECS department at George Washington University, noted in her opening talk that while numerous quantitative studies document the absence of women at different levels of the science career pipeline, few qualitative analyses demonstrate the dynamics of the processes that keep women out. Terry Russell (in this volume) notes a similar paucity of research on the experiences of minorities at higher levels of academia and in industrial laboratories. During the late 1980s, after the National Science Foundation expressed concern that too few Americans were entering scientific careers, it was concluded that some of the shortfall could be made up from traditionally underrepresented segments of the population such as women and minorities. At the end of the Cold War, however, the overgrown scientific and engineering establishments in Eastern Europe and the former Soviet Union began experiencing a “brain drain” as their science personnel sought better opportunities in the United States and elsewhere. Some will stay permanently, others will return after short study or research visits, and still others fall into the category of “double-life scientists” (Balazs, Pallo, and Etzkowitz 1994) who divide their time between institutional bases in their homeland and abroad. Apart from these unexpected migrations, the projected shortfall of American scientists and engineers proved to be exaggerated, thereby reducing the pressure to develop new sources of scientific and engineering talent. Perhaps even more dispiriting than the lack of incentive for recruitment is the movement toward downsizing that has caused minorities and women to be laid off in disproportionate numbers according to the dictum “last hired, first fired.” As one black engineer described his separation from a defense company in the early 1990s, “When the time comes for cutbacks, these large contractors start chopping from the bottom and that’s where you’ll find most minorities because they are often the last hired” (Sims 1993). Despite generally unfavorable trends, some companies took steps to retain minority employees by overriding seniority rules, laying themselves open to age discrimination suits. In an expansionary period, it is easier to attract, retain, and promote minorities and women; in a period of contraction the effort is harder. But since barriers to equality continue to exist, even in favorable economic climates, there is every reason to persist in efforts to overcome them.

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Research on barriers to women in graduate education in the sciences (Etzkowitz, Kemelgor, and Neuschatz 1989) found that discrimination often occurs at the upper levels of the doctoral training process within the advisor-advisee relationship. Faculty members make individual subjective judgments about the validity of graduate students’ scientific work as opposed to the collective decision making and more uniform criteria used in evaluating qualifying examinations. Whereas the exam often gives women positive reinforcement about their technical capabilities, it is hard to argue with a dissertation advisor about a negative evaluation. Women may accept dismissive judgments about their work without seeking alternative opinions, especially if they have low self-confidence (Etzkowitz, Kemelgor, Neuschatz, and Uzzi 1992). Far from being gender-neutral, academic science has incorporated certain features into its ideology and organization that can impede or flush out large numbers of qualified women (Etzkowitz et al. 1992; Talapessy 1993). There is increasing recognition that the academic structure does not facilitate passage for women. And for women who aspire to scientific careers to succeed requires truly heroic efforts (Abir-Am 1991; Etzkowitz et al. 1992, 1994). Nevertheless, some observers characterize the student experience as essentially comparable for all, regardless of gender or race, and view different outcomes as the result of personal choice (Cole 1979). However, the consistency of the patterns suggests that they are in fact largely structural and institutional in character. Individual experiences within the Ph.D. training system must be examined in order to direct attention toward program organization and management and issues of structural reform. The strength of these negative forces ensures that only those with the strongest motivation, support, and financial resources will persevere (Ruivo 1987). Recent work on women in science and engineering has moved from the analysis of data on differential rates of participation to capturing the experience of underrepresented groups in the academic enterprise. A crucial finding is that women experience the academic system distinctly from men and their careers are structured in important ways by how that system operates. For example, women tend to have difficulty developing mentoring relationships that are crucial to career advancement (Science 1992). A study of women enrolled in a chemistry doctoral program with few women on the department’s faculty showed that the students lacked faculty role models and mentors and compensated by gaining direction from older students and peers (Etzkowitz and Kemelgor 1994; Mergler 1992). A more extensive investigation across a number of departments found that despite the willingness of many women and minority faculty to mentor graduate students, these faculty members often do not have adequate time to devote to such efforts, especially in the early stages of their career (Etzkowitz, Kemelgor, and Neuschatz 1989).

Recruitment to Science The very small percentages of non-Asian minorities in the sciences and engineering suggest that the few who attain the Ph.D. represent individual instances of recruitment to science that are idiosyncratic and happenstance even as the barriers against them are systematic and taken for granted. For example, an African American Ph.D.

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in chemistry recalls how he got started on the path to higher education in science. In high school, “the teachers didn’t perceive me as college bound. I really didn’t get any counseling.” He likely would not have gone to college without the chance intervention of a classmate, who “advised me when the University of Illinois was enrolling students.” The chemist ended up in a line in which the students behind him were talking about the engineering, chemistry, and physics majors being the hardest. “I had [taken] physics so I had confidence, naive confidence,” he recalls. Another respondent credits the origins of his decision to become a scientist to an elementary school teacher who gave him confidence in his abilities: “I was the brightest student. They were testing us and I tested higher than everyone in the county, black or white.” Nevertheless, these abilities could have become submerged, without encouragement. A teacher gave him special books, tutored him in grammar, and was emotionally and intellectually involved in his education. “I think she liked math a lot. She fed my own abilities and interest in math,” he says. The intersection of minority and female students with people who glancingly or deeply point their way toward science helps explain the recruitment of many of these unusual individuals to scientific careers. Why are there so few? According to another respondent, “It is difficult to convince kids that it is an option, that is one reason why there aren’t as many. Kids don’t think that they can be scientists. They have not thought about it as an option they can consider. They don’t see anyone who does it. Although this is especially true of minority kids, it is generally true.” He describes the reaction he often received from minority children when he visited schools: “‘Wow you don’t look like a scientist.’” He adds, “They never imagine a black guy as a scientist. They have a ‘nutty professor’ view of someone as a scientist.” Internalized images of the scientist as an unlikely career role for a member of a minority group are reinforced by external barriers to entry. Often, the family provides a warning of the difficulties that will have to be confronted in pursuing a nontraditional career. Parental advice to a young African American entering “white” fields like science is: “You have to be better than the best whites to succeed. You will be held to a higher standard.” The black female chemist who received this warning notes that “fifteen years ago you didn’t have women doing process engineering.” At the time she felt that she was considered “an oddity, more because of being a woman than being black.” Nevertheless, some persons subject to discrimination may soften their perception of the negative environment they are operating in if they achieve some modicum of success. Others follow the traditional sociological path, gaining a sharper social perception as an outsider. Who Is Mentored? An individualistic as opposed to a group response to a negative situation can be identified in which the issue of minority status and its potential negative impact is ignored and reliance is placed wholly on meritocratic criteria. As one respondent expresses this strategy: “My attitude is just do good work and the rest will take care of itself.” The expectation is that you are on your own. The respondent says, “I never expected anyone to give me the lamp and say this was the path.” Her advice to young minority and female scientists from her career experience is, “Once you have got-

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ten through graduate school, count on yourself or shame on you. You have missed out on a major lesson.” But having others to count on is typically how promising, young white male scientists are treated. The norm is that they are mentored. According to one respondent, “Mentoring in an industrial environment is not the same for bright white males as for black males. There is more social interaction, more lunches together, more career counseling, more scientific suggestions, more scientific dialogue.” Given the relative absence of such a support structure, only the most outstanding women and minority scientists survive and advance under these bleak conditions. The Debilitating Effects of Isolation Stigmatization, isolation, and loss of confidence constitute a mutually reinforcing cycle that depresses the performance of women and minorities in science. A successful African American scientist recalls his reaction to learning that his advising folder contained the statement, “This student should be discouraged from continuing in physics.” He became depressed. “I couldn’t sit in class, and when I made it to class, I had the urge to bolt to the door. Now, I recognize it as depression.” Failure to recognize what is taking place, at the time, effectively precludes both individual and collective response. In another instance of unawareness, a black scientist recalls his experience in finding lab partners. “I would end up with a partner now and then.” [Interviewer: Did you feel isolated?] “I don’t remember having an emotional feeling that way. I hated labs. I probably had one black partner in all that time. It may, in hindsight, have been isolation. I was very isolated.” Sometimes, a perceptive and sympathetic advisor provides at least a partial remedy for isolation. An African American scientist recalls how his thesis advisor visited him in the lab on Sundays when he was a graduate student. With this extra encouragement, having to work alone “didn’t impact on me in the way it did on other [minority] students.” However, his advisor warned him to expect a bleak future. “He told me, ‘When you leave here no one is going to work with you.’ It never occurred to me to ask him why.” Partially insulated by his advisor, the student didn’t fully appreciate the issue of isolation at the time. One of the biggest problems that minorities and women face is that they are excluded from full participation in the informal life of science. The issue of isolation goes beyond the debilitating effects of lack of social interaction. According to one respondent, “It is a matter of . . . having one’s rate of growth controlled. I tell students how important it is to interact, if you are interested in the sciences.” Despite mythology to the contrary, “No one is going to pop up with an idea to win the Nobel prize totally on their own,” he says. In science, a group often decides on common goals and implements research strategies collectively. Thus, it is important for a heretofore excluded group to master the social processes of good science and become a part of its social networks. Members of a broad scientific network gain confidence through their participation. They also accumulate an extensive curriculum vitae. A minority scientist, viewing the collaborative research process as an outsider, comments that while some such insiders “co-author hundreds of papers, you are lucky to get twenty by yourself.” The

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accumulation of authorships and citations feeds upon itself and insures a presumption of scientific acuity despite the fact that “when someone has a resume with 100, 200, or 300 articles, it is difficult to define what their real effort was.” Although there is resistance to minority participation in mainstream institutions, a respondent also points out the difficulties of doing science in the isolated environment of historically black colleges where it is “very difficult to be productive in science.” Resistance to Acceptance of Minorities and Women Respondents express mixed feelings about whether conditions have improved. One feels that “once you demonstrate competence and knowledge, you move on.” Another finds that despite overcoming initial barriers, there is still a reluctance on the part of peers to grant full acceptance. He believes blacks are less likely to be accepted as informal leaders of research efforts, the precondition for formal advancement in the laboratory. This resistance at the working scientific level has a dual negative effect, on the one hand undermining the minority scientist’s confidence, and on the other, reinforcing the stigmatized view of minority scientists as inappropriate choices for leadership positions. Resistance to acceptance is reported in a variety of venues, inside and outside the laboratory. For example, one respondent says, “As a woman I have had a bad experience at conferences. They assume I don’t know what I am talking about.” At her workplace, a large think-tank, she says, “I’ve had eyebrows raised from people who didn’t expect a black woman [in a senior position].” People occasionally assume she is a secretary and ask her to perform tasks. “They cannot make that leap that I am their colleague.” On the other hand, she finds that the predominantly minority support staff are impressed and very pleased to see someone who shares their background in a high position in the organization. Many scientists are reluctant to publicize negative experiences out of concern that it may discourage fellow minorities and women from pursuing a scientific career. A female scientist who worked in a woman researcher’s laboratory one summer recalls her ambivalence after hearing “horror stories about the experience of women and blacks in laboratories.” On the hopeful side, this scientist believes that “people who are interested in teaching you the ropes will tell you what you need to know.” Another colleague from an ethnic background offered her useful advice on how to deal with the closing of the ranks: “He told me I had to watch my back. People who smile in your face may stab you in the back.” Issues of diversity and concerns about incorporating minorities in the sciences may have peaked in the early 1970s. One respondent feels that a high point was reached in his laboratory at the height of the Civil Rights Movement in the late 1960s: “Because of the social changes occurring in that period of time, I think that my white peers were more open to a diverse environment than probably any other generation before or after.” His analysis is based not so much on numbers hired but on the willingness to incorporate minorities in the everyday life of the lab. He suspects that his successors have had a more isolated career in the lab than he experienced. This senior scientist describes the ambivalent effect of efforts to assist minority scientists. When he joined the lab he was given support by upper management. Al-

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though this didn’t sit well with members of the lower level peer group, they included him in projects, nevertheless. This inclusion was crucial to his success since the sciences demand collaboration. However, he recalls that “the longer I was there, new researchers were less and less tolerant of minorities.” A respondent in academia notes that minority group members can partially overcome the debilitating effects of isolation by organizing themselves in places where there is at least a minimum critical mass. There is a need to change the academic and industrial science system to provide mentoring and an inclusive social atmosphere. Women and minorities need this most because they are often excluded from informal networks, but a more supportive system benefits all. The author’s studies of women in science have identified several aspects of critical mass, including the effect of a strong minority presence in academic institutions on organizational behavior and on the minority group itself (Etzkowitz, Kemelgor, Neuschatz, Uzzi, and Alonzo 1994; Etzkowitz and Mulkey 1990; Mulkey and Etzkowitz 1991). As a complementary category to historically black institutions of higher education, universities and colleges that have gained significant minority enrollment in recent years, for example, City, Hunter, and Brooklyn Colleges in the City University of New York, merit investigation as a potential source of critical mass for minorities in science. Rather than being denoted by a particular percentage, a recently black college or university may emerge in stages, first as a critical mass, a group that perceives itself as an entity, and later is perceived as such by others. Or it may be one possible outcome of a transition; a multicultural college or university is another possibility. An ethnic succession could take place in which several groups attain a visible presence (Etzkowitz, Kemelgor, Neuschatz, Pearson, and Leggon 1994).

Critical Transitions Russells’s intriguing “pinball” model with room for “emergence, chance, interactive effects and ‘three dimensional movement’” captures the essential elements of industrial R&D career progression from research to management. This model can be applied to focus on the points at which decisions are made about movement into higher levels of research management. A first step is to identify the decision points. The second step is to organize a research program including in-depth interviews with minorities and women who are currently at, or have recently experienced, these decision points as well as those who make the decisions. Such a program of investigation within a range of industrial sectors, old and emerging technological fields, laboratory sizes, and so on, could provide a systematic view of the research questions raised by Russell (1997) and give rise to policy measures to overcome barriers that are identified. Ph.D. Programs In academia, it is well known that graduate education is not a smooth continuum, with a steady rate of leakage from the pipeline, but rather a discontinuous, turbulent flow, with attrition rates rising at certain key junctures. There are several specific points

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in the career trajectory when people are propelled forward, pushed out, or dropped down to a lower level. Investigation is likely to reveal specific gates, critical transition points where attrition is highest. These processes are not uniform but are strongly affected by degree program organization and structure. For example, two standard models for running a graduate programs—“weeding out” and the “member of the family” approach—are likely to produce different outcomes. In the former model, each step of the Ph.D. process is viewed as an opportunity to decide whether the candidate should proceed to the next step; the assumption is that a larger number of persons have been admitted than are actually worthy of achieving the degree. In this type of program, deselection is normative. In the latter model, the critical decision point is viewed as having been made during the admissions process; all who have been selected to enter the program are deemed worthy of receiving the degree. Only exceptional circumstances should displace anyone from being a “member of the family.” In one chemistry program surveyed, the dissertation was defined as the publishable papers that a student had written during a certain time span. In a physics program, research accomplishments were viewed as an acceptable justification for relaxing the standard for passing qualifying exams, since that was seen as the purpose of the program. These findings reinforce the utility of a focus on departmental organization and culture as a framework for analyzing individual student experience. As a first step toward developing a longitudinal analysis of women and minority experience in Ph.D. programs we should focus on the critical transitions that are explicitly or implicitly built into the structure of graduate training. Some critical transitions are highly structured, with clear benchmarks; others are more informal, with loose or shifting criteria. At the Ph.D. level, these transitions are likely to include: 1) the qualifying examination, 2) finding a research adviser, 3) negotiating a dissertation topic, and 4) deciding what is sufficient work for granting the degree (Etzkowitz, Kemelgor, Neuschatz, Pearson, and Leggon 1994). Some transitions, such as finding a research adviser, differ by discipline; for example, in biology the custom of rotation among laboratories during the first year typically introduces the entering student to three professors and their research practices. In other instances, negotiating a transition is highly dependent on access to informal sources of information. The decision to apply to graduate school is a critical transition that typically takes place in the junior year of college. The job search undertaken toward the close of a successful Ph.D. career or a terminal master’s degree program are also critical transitions. The first job change represents a key transition point (Sonnert, 1990). Transition points in Ph.D. programs include both formal mechanisms of evaluation, such as the qualifying exam, that are built into the academic system, and informal transitions, such as finding an adviser and/or mentor, that do not have explicit benchmarks associated with them but nevertheless are crucial to successful advance in a scientific career. Critical transitions are believed to affect women and men differently. Previous socialization and gender-specific obstacles in the system may account for some of these differences. A similar series of differential effects is likely for minorities. Respondents for the study should include those who have left graduate programs and jobs as well as those who have remained (Etzkowitz, Kemelgor, Neuschatz,

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Pearson, and Leggon 1994). The ability to identify those who have left programs is related to the structure of the program. Highly structured programs with explicit timeframes will make it easier to identify those who have not completed the degree requirements. For example, a program structured so that everyone must take a preliminary examination at the end of the first year and is given a second year to pass all sections of the exam makes it easier to identify who has not met the requirement at the end of the first and second years. A less-structured program makes it difficult to distinguish between students who drop out of the program never to return and those who take time off from the program to “get themselves together” and then return to graduate study. Research Questions. The proposed longitudinal analysis of women and minority experience in the Ph.D. programs might include the following questions: • What are the transition points in the Ph.D. program as identified by the head of the graduate studies program and official documents; and as identified by women students at different points in their graduate career? What are the points of coincidence or disjuncture between official and student perspectives and within the female student sample at different points in the graduate career? • How do transition points work to facilitate movement to the next level? Conversely, how do they operate to deter mobility? • How do students and graduates assess their transition experiences prospectively and retrospectively? Did the experience meet expectations? Did the department prepare students adequately for the transition? Was the experience reinterpreted later (favorably or unfavorably) by participants who left the program? • Has the Ph.D. program in a given department been restructured in recent years? If so, why? What was the process? Who initiated the change? Who opposed it? How was the change introduced? What has been its effect as seen from the different perspectives of faculty and students (women, minorities, and men)? • What student behavior does the department reward: originality in research; results; following faculty research designs; developing one’s own initiatives in organizing research? Do rewarded behaviors differ for women and men? • Is there a department-wide procedure for evaluating student progress toward the Ph.D. or is it left to individual advisers? How are disputes over evaluation of student progress resolved; is there a formal appeals process? What has been the experience of women and minorities with this process? Industrial Laboratories Industrial laboratories have a relatively well-structured career path; even so, there are subtle informal elements. The first managerial assignment in a large industrial laboratory may not even be a formal position. Among a group of six or eight junior

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personnel a few years out of school, an individual may be designated as group leader based on a combination of technical knowledge and the ability to work with people. The ability to communicate and get along is what makes an individual stand out; so does a reputation as a “natural problem solver” (Fusfeld 1995). As an individual moves up the managerial ladder, interpersonal criteria become more important relative to technical expertise and formal disciplinary background. For example, a physicist with managerial abilities may be assigned to direct a chemical project. In the movement to still higher levels of executive administration, nonscientific skills, such as the capacity to make business and financial decisions, become ever more important. The critical transitions in an industrial science career involve a crossover from scientific and technical to human relations and business skills. The Women’s International Technical Institute, a membership organization of women industrial scientists and engineers, has expressed interest in having a qualitative study performed of women in industrial laboratories (Leighton 1993). Such a study, along the lines of the initial small-scale study of women in science in four disciplines at two universities, would be of great interest to female industrial scientists and engineers and their employers (Etzkowitz, Kemelgor, Neuschatz, and Uzzi 1992). A parallel study of minorities in industrial laboratories would also be an important contribution.

Conclusion The continuing underrepresentation of minorities and women in most fields of academic and industrial science and engineering has been widely recognized as an issue of equity and underutilization of human resources (Lane 1994; Pearson and Fechter 1993). Neal Lane, the former director of the National Science Foundation, has shifted the focus to one of scientific “self interest”: justifying the use of public resources for science by demonstrating that scientists represent a closer approximation to the demographic composition of national populations (Etzkowitz 1994). The loss to science and engineering of members of underrepresented groups who have already moved a considerable distance through the “pipeline” is a matter of special concern given the high level of motivation and interest already demonstrated and the lack of full return on society’s investment in critical human resources. It is necessary to determine more precisely the factors that contribute to attrition and to identify strategies of intervention to improve retention in Ph.D. programs and in academic and industrial careers.

Selected References Abir-Am, P. 1991. Science policy for women in science: From historical case studies to an agenda for the 1990s. Paper presented at History of Science Meetings, Madison, WI, November 2. Alonzo, J. Women in science: An international analysis. B.A. thesis. SUNY, Purchase, Department of Sociology.

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Balazs, K., G. Pallo, and H. Etzkowitz. 1994. Double-life scientists. Proposal to the Soros Foundation. Cole, J. R. 1979. Fair science: Women in the scientific community. New York: Free Press. Chubin, D., and E. Robinson. 1992. Human resources for the research work force: U.S. indicators and policy choices. Science and Public Policy. Dowdall, J. 1978. Mentors in academe: The perceptions of the protege. Paper presented at American Sociological Association Annual Meetings. Etzkowitz, H. 1991. Small science in crisis. Report to the National Science Foundation. ——— . 1992. Individual investigators and their research groups. Minerva. ——— . 1993. Women scientific entrepreneurs: Overcoming the marginalization of women scientists and engineers in industry and academia. Paper presented at CWSE National Academy of Sciences Conference, Irvine, CA, January. ——— . 1994. The role of NSF in science policy and of scientists in the politics of NSF. Technology Access Report, February. ——— . Academic entrepreneurs and their research centres. Minerva, forthcoming. Etzkowitz, H., and M. F. Fox. 1992. Women in science and engineering: Improving participation and performance in doctoral programs. National Science Foundation grant. Etzkowitz, H., and C. Kemelgor. 1994. Critical transitions: A longitudinal study of female Ph.D. students in science and engineering departments. Proposal to the Sloan Foundation. Etzkowitz, H., and L. Mulkey. 1991 [1990]. What you know vs. who you know: The role of social and cultural capital in the recruitment of women to scientific careers. Presentation at A.S.A. annual meetings and a proposal to the NSF. Etzkowitz, H., and P. Stein. 1978. The life spiral: Human needs and adult roles. Alternative Life Styles, Fall. Etzkowitz, H., C. Kemelgor, and M. Neuschatz. 1989. The final disadvantage: Barriers to the recruitment of women in science and engineering. A report to the National Science Foundation. Etzkowitz, H., C. Kemelgor, M. Neuschatz, and B. Uzzi. 1991. Restructuring departments for equality: Gender inequities in academic science. In D. Martin, ed., International society for technology in education. ——— . 1992. Athena unbound. Science and Public Policy 19(3): 157–179. ——— . 1994. Barriers to women in academic science and engineering. In Pearson and Fechter, eds., Who Shall Do Science? Baltimore: Johns Hopkins University Press. Etzkowitz, H., C. Kemelgor, M. Neuschatz, B. Uzzi, and J. Alonzo. 1994. The paradox of critical mass for women in science. Science October 7. Etzkowitz, H., C. Kemelgor, M. Neuschatz, W. Pearson, and C. Leggon. 1994. Critical transitions and barriers to success: Woman and non-Asian minorities in science and engineering. A proposal to the NSF. Fox, K. 1993. Minority networks forge bonds in chemistry. Science 262(12): 1126. Fusfeld, H. 1995. Career paths in industrial laboratories. Interview with Henry Etzkowitz, January 24. Kemelgor, C. 1989. Research groups in molecular biology: A study of normative change in academic science. B.A. thesis, SUNY, Purchase. Lane, N. 1994. Science policy. Address at Columbia University. Leighton, C. 1993. Personal communication with Henry Etzkowitz, January. Lusterman, S. 1979. Minorities in engineering: The corporate role. New York: The Conference Board. Mergler, L. 1992. Gender distinctions in the scientific community. SUNY, Purchase, Department of Sociology.

306 Early Career Preparation: Industry Moen, P. 1988. Women as a human resource. Washington, DC: National Science Foundation, Sociology Program, Division of Social and Economic Science. Mokros, J. R. et al. 1980. A new role for professors. College Board Review, Winter: 2–5. National Science Foundation. 1984. Women and minorities in science. Washington, DC: Author. ——— . 1988. Women and minorities in science. Washington, DC: Author. Pearson, W., and A. Fechter, eds. 1993. Human resources for science. Baltimore: Johns Hopkins University Press. Ruivo, B. 1987. The intellectual labor market in developed and developing countries: Women’s representation in scientific research. International Journal of Scientific Education. Science. 1992. Special Issue on Women in Science, March 13. Sims, C. 1993. Last hired, first fired? Minorities retreat in defense. Science 262(12): 1125– 1126. Sonnert, G. 1990. Careers of women and men postdoctoral fellows in the sciences. Paper presented at American Sociological Association Meetings, August. Talapessy, L., ed. 1993. Women in S/T research in the EC. Brussels: Commission of the European Communities, Directorate-General for Science, Research and Development. U.S. Congressional Office of Technology Assessment. 1991. Federally Funded Research: Decisions for a Decade. Washington, DC: U.S. Government Printing Office. Vetter, B. 1994. Who is in the pipeline? In W. Pearson, Jr., and A. Fechter, eds., Human resources for science. Baltimore: Johns Hopkins University Press.

george campbell jr.

Critical Issues NACME’s fundamental mission and role in preparing African American, Latino, and American Indian students to enter the engineering workforce, coupled with its historical partnerships with the corporate sector, make it uniquely sensitive to the issues faced by minority graduates embarking on careers in industry. Its undergraduate programs provide not only financial aid but comprehensive support, which must take into account the barriers and impediments faced by minority students. It’s essential for us to know what the critical issues are and to understand them in a comprehensive way. Following a brief overview of NACME’s responsibilities in the career development of minority engineering students, I’d like to offer several observations based on NACME’s collective experience with corporations, supplemented by my personal experiences in the corporate sector (my twelve-year tenure at AT&T Bell Laborato-

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ries). Although these observations are largely anecdotal, reflecting a lack of systematic studies in this area, they may still suggest important avenues of research or uncover gaps in our knowledge base that have substantive policy implications. A major underlying factor in NACME’s quest to increase the representation of African Americans, Latinos, and American Indians in engineering is the high attrition rate of engineering students who are members of these population groups. Reginald Wilson noted that a major cause of this high attrition rate is the lack of personal financial resources, which is compounded by shifting financial aid policies that reduce scholarships available to poor students. It is precisely because of this finance-driven attrition that NACME has maintained an absolute commitment to scholarship programs. Unfortunately, the growth in its budget has not kept pace with its enormous success in attracting minority high school graduates to engineering, coupled with the dramatically rising cost of higher education. NACME now provides scholarships for a third fewer students than it did a decade ago, although the average amount of the grant per student is larger. Nevertheless, it still supports two thousand students annually and continues to offer the largest private scholarship program for minority engineering students in the United States. The goal of NACME’s scholarship programs is not only to ensure that all of its students successfully complete their degrees but to enable them to function at the highest academic levels and prepare them for successful careers beyond the degree. Given the environment they’re in, this means providing a broad range of support beyond financial aid. For example, the Corporate Scholars Program (CSP) requires each sponsoring company to provide a mentor for each student, chosen from the ranks of their engineering or technical management professionals. The mentors are trained by NACME to ensure their effectiveness. Students are awarded summer internships, which are monitored to make certain they provide relevant assignments that contribute to students’ professional development. The program also offers a range of academic enrichment, career development, and leadership-training workshops. Based on NACME’s surveys of students and corporate participants as well as on quantitative results (e.g., academic performance of students, retention rates, number of students hired by sponsoring companies, number of students who pursue graduate degrees at top-ranked institutions), we know the program is highly successful in providing engineering graduates with excellent credentials. Half of all CSP students attend highly rated graduate programs. The retention rate is about 85 percent. The average grade point average (GPA) of graduates is above 3.3, with 20 percent exceeding 3.8. What we don’t know at this stage is what will happen to these outstanding young graduates in their professional careers in industry. NACME has been interacting with the companies in its program to get a handle on the ingredients for a successful career, particularly for minority employees, and these have been built into CSP workshops. What I’d like to see, however, is the establishment of a strong research foundation to track the career paths of minority engineering graduates. And, just as we’ve done for universities, I’d like to look at both sides of the equation, that is, determine which corporate cultures and attributes are most conducive to the success of minority scientists and engineers, as well as which attributes and skills are required of the employees themselves in order to excel in industry careers.

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In developing CSP in its early stages, NACME drew heavily on my experiences at Bell Labs and on its long-term experience with a range of major corporations. Let me share some of these experiences and observations. The first assignment. Just as there appears to be a correlation between first-semester GPA and ultimate undergraduate success, I believe that an analogous pattern exists in employment, namely that there is a high correlation between success in the first project—whether it’s a research effort at the doctoral level or an engineering design assignment at the bachelor’s level—and a successful career path in industry. This looms large for new minority employees; like minority students, they suffer disproportionately as a consequence of the low expectations that their managers harbor. This phenomenon seems to persist even when minority employees come equipped with extraordinary credentials. At a company with which I’m very familiar, which seldom looked at students having a GPA below 3.5 at the nation’s most selective institutions, there was real skepticism on the part of managers about whether new minority employees could really “cut the mustard.” Their initial assignments reflected these lower expectations. They tended not to be in the mainstream, not the high-visibility jobs essential to success, not in the critical path of a major development or research effort. Managers were not willing to take the risk and jeopardize their own career advancement by trusting a new minority scientist or engineer to get the job done. Minorities in that kind of environment who have been successful had to ignore the constraints of their work set by management. They had to find problems to work on that would allow them to insert themselves into the mainstream. The irony here is that in a very competitive environment this approach is very risky. If the employee is successful and solves an important problem, the boss forgets about what the initial assignment was, but if he or she is unsuccessful, not only is the employee seen as incompetent but also as one who can’t follow directions. The right to fail. After a successful project, it is natural for employees to acquire a broader space within which to operate. The right to fail has been earned. This is critically important because no matter how good the scientist or engineer, not all R&D projects bear fruit. Albert Einstein spent the last half of his life trying unsuccessfully to reconcile general relativity, the theory of gravity, and quantum theory. Unfortunately, everyone doesn’t have an equal right to fail. When a previously successful nonminority employee fails, it’s considered bad luck. When a previously successful minority employee fails, the prior success is considered good luck. The failure then simply confirms the previously held low expectations. Reputation. Minorities—even those with a long history of success—typically do not carry their reputations with them when they move from one position to another within a company. As they move from place to place or work for new management at the same place, they have to prove themselves all over again. Mentoring. In an admittedly unscientific survey at several companies, I asked new employees how much time they spent with their respective supervisors each week.

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The results showed an astonishing variation. Minority employees spent about half as much time with their supervisors as did nonminority employees. This reflected what was often referred to as a lack of comfort level between white male managers and minority employees. A broader, related issue is that in order to advance in the corporate community, it is absolutely essential to have a mentor, someone who is highly successful, who knows the ropes, who has access to information, and who is a strong and willing advocate. Such mentoring relationships do not develop naturally for minority employees. Performance feedback. A major issue that emerges from the lack of comfort level between white managers and minority employees in the corporate R&D community is that minority employees often do not get honest, direct, constructive, and timely feedback on how they’re doing. It is not uncommon for minorities to be under the impression that they’re doing a great job, only to discover, late in the year, that they’re perceived as mediocre contributors, or that when they’re told, “You’re doing great,” what is really meant is, “You’re doing great, compared to what I expect from someone of your ethnic heritage.” In the formal performance-review process, I have seen strong evidence of a bimodal distribution of minorities as the norm. That is, minority employees are either superstars or at the bottom of the ladder. They are not allowed to be average. If minorities don’t overachieve, they don’t get decent assignments and the already low expectations spiral downward. Peer support. This is obviously essential for success in an engineering career. Science and engineering cannot be done in isolation. It is necessary to have a peer group that is willing to share information, work collaboratively, and embrace you as a team member. The peer group has to believe you’re bringing something to the table to contribute. Here again, the lack of comfort in communicating across ethnic and gender boundaries creates difficulties. Low expectations of individuals collaborating on a project inhibit teamwork. Diversity in management. Just as there is a need for minority faculty to alter the environment for undergraduate and graduate education in order to establish a healthier climate for all students, more minorities in positions of responsibility and authority in the corporate R&D sector would have a positive impact on corporate culture. Greater diversity not only improves the working conditions for minorities but enhances overall corporate performance. Several studies have shown that, based on objective measures of performance such as creativity, problem-solving skills, effective decision-making, and productivity, diverse groups have significantly higher potential than homogeneous groups (DiStefano and Maznevski 1993). Their longterm study of groups in international business indicates that diverse groups with effective interactive skills are the highest performers. In a brief but comprehensive review of research on the subject, Taylor Cox and Stacy Black (1991) cite a number of studies that corroborate these findings. Rosabeth Moss Kanter (1983) found that innovative companies consciously form heterogeneous

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teams which generate a broad spectrum of ideas and a range of approaches to problems, increasing the probability of creative solutions. Studies at the University of Michigan quantified the higher probability of getting better performance from diverse groups. In controlled experiments, Charlene Nemeth (1986) also found that diverse groups produced more creative outcomes than homogeneous groups with similar ability. Cox and Blake summarize Nemeth’s findings on decision processes: In a series of studies, she found that the level of critical analysis of decision issues and alternatives was higher in groups subjected to minority views than in those which were not. The presence of minority views improved the quality of the decision process regardless of whether or not the minority view ultimately prevailed. A larger number of alternatives were considered and there was a more thorough examination of assumptions and implications of alternative scenarios (51).

Cox and Black point out that diversity generates greater organizational flexibility— cultures and structures that are less standardized and more fluid, agile, and adaptable. Organizations with these attributes can react much more quickly to changes in the environment, essential in today’s world. Promotion. What does it take to get promoted in the corporate R&D laboratory? In a highly competitive environment, there are always a number of highly qualified candidates whenever a promotion opportunity emerges. To be selected one has to have strong advocates, an immediate supervisor who is willing to stand up and argue forcefully—even contentiously—on your behalf, to promote your attributes over those of members of his or her own ethnic group, a manager whose objectivity and sense of fairness supersede his or her group identity. Again, these are some of the more blatant obstacles I’ve observed anecdotally. Clearly, there is a gap in our base of knowledge. We don’t have systematic, longitudinal data on large samples of minority SEM employees in industry. Because of the higher attrition rates of minority employees, some companies have collected information on their employees and have taken a hard look at their internal processes; for obvious reasons the data is considered highly proprietary and is guarded very closely even though studies could be designed to yield substantive data while maintaining the integrity and anonymity of participating companies. In the spirit of benchmarking, best practices, and continuous improvement, I believe that one could develop a pool of companies from NACME’s partners that would gladly cooperate in such an undertaking. What we need to know is what is really happening to minority SME employees. What are their retention rates as employees? How often do they change jobs? What are the promotion rates and how do they compare with their nonminority peers? How well are they compensated, as compared to their nonminority peers? We need quantifiable data that can either confirm or deny the statistical significance of the obstacles I’ve described anecdotally. We need to know if there are other critical issues that still remain to be explored. Finally, we need to identify solutions. We need to look at those programs, strategies, and corporate cultures that are most successful in developing and maintaining a world-class, diverse scientific work force, one that embraces the talents of every segment of the American population.

Models for Studying Early Careers: Minority Scientists and Engineers in Industry Policy 311 References Cox, T. H., and S. Blake. 1991. Managing cultural diversity: Implications for organizational competitiveness. Academy of Management Executive 5(3): 45–56. DiStefano, J. J., and M. L. Maznevski. 1994. Effective management of diversity: A theoretical model with empirical evidence. Paper presented at the American Association for the Advancement of Science Annual Meeting. Moss-Kanter, R. 1983. The Change Masters. NY: Simon and Schuster. Nemeth, C. 1985. Dissent, group process, and creativity. Advances in Group Processes 2: 57–75.

willie pearson, jr.

Policy History has shown that even when African Americans receive their scientific education from the most prestigious doctoral programs in the country, structural and institutional barriers can serve to limit their careers. The careers of Charles H. Turner (1967–1923), Ernest E. Just (1883–1941), William Hinton (1883–1959), and Percy L. Julian (1899–1975) are classic examples of African Americans of exceptional scientific talent whose careers were limited in some manner because of their race. The biographies of these early scholars reveal that despite their many scientific accomplishments, their careers largely developed outside major research universities or laboratories. In 1907 Turner earned a doctorate in entomology at the University of Chicago. Most of his research focused on the behavior of bees and ants in general and their social organization in particular. During his career, Turner is credited with publishing nearly fifty articles in major scientific journals. He maintained this high level of productivity despite spending most of his career teaching in high schools; he reportedly had a single collegiate appointment at Clark, a black college. Just also earned a doctorate in biology from the University of Chicago in 1916. Just spent most of his career at Howard University in Washington, D.C. However, his summers were devoted to conducting research at Woods Hole, Massachusetts, where he primarily engaged in leading-edge research on artificial parthenogenesis. During his career, Just was considered one of America’s most eminent scientists, having rated a star for distinction in early editions of American Men of Science. Few scientists, regardless of race or ethnicity, received such an honor. During his career, Just published two books and more than sixty articles in major scientific journals. Despite

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his numerous scientific honors and achievements, Just was never very comfortable with his status in the U.S. scientific community primarily because he could never escape the stigma of his race (Manning 1983). According to his mentor, the eminent biologist Frank R. Lillie (1942), Just never received a full-time appointment to a major research university in the United States because of his race. Hinton, a contemporary of Just’s, earned his doctorate from Harvard in 1912. He specialized in serology. Hinton began his career as a pathologist and subsequently became director of the Wasserman Laboratory at the Massachusetts Department of Public Health. He left this position to become a pathologist at the Boston Dispensary and a part-time faculty member at Harvard Medical School, where he taught bacteriology. During his career, Hinton was recognized as one of the world’s leading authorities in the field of diagnosing and treating venereal disease. Nevertheless, Hinton held a nonteaching position for much of his career at Harvard (Franklin 1966). Julian received his doctorate in organic chemistry from the University of Vienna in 1931. He subsequently returned to Howard University, where he chaired the department of chemistry and began his research on the structure and synthesis of physostigmine, a drug used to treat blindness resulting from glaucoma. He left Howard to accept a research and teaching position at DePauw University, his alma mater. It was at DePauw that Julian received both national and international acclaim for his synthesis of physostigmine. Because of this and other scientific accomplishments, Julian was hired by the Glidden Company to head its research unit. This was a signal achievement, for it marked the first time that an African American Ph.D. scientist had been selected to direct a modern industrial laboratory. Julian subsequently left Glidden to found his own laboratory. He is credited with publishing approximately one hundred papers and registering about fifty patents. Despite his achievements, Julian’s checkered career was marked by various racial incidents within the scientific community. For example, when Dean Blanchard wanted to appoint Julian as head of DePauw’s chemistry department, the faculty felt the appointment was inadvisable (Haber 1970, Klein 1971). Because of the racial discrimination faced by these accomplished scientists, the United States will never know how much additional knowledge these scientists could have contributed to the field had they been free to develop their full creative abilities. While these cases have been cited frequently as evidence of racial discrimination in the area of science, they represent a period of American history where both racial discrimination and segregation were widespread. However, more recent evidence suggests that most African American doctoral scientists believe that while racial discrimination in the scientific community has changed considerably, race still matters in terms of employment and career development. In a study of American doctoral scientists, Pearson (1985) found that regardless of cohort, most African American respondents believed that their career mobility had been limited in some way by their racial status. However, the proportion of scientists responding in this manner declined over the cohorts. For example, 74 percent of those earning doctorates before 1955 believed that their career mobility was limited because of race, compared with 60 percent of those earning doctorates

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between 1965 and 1974. These findings suggest significant changes in respondents’ perceptions of the opportunity structure in American science. Such response patterns held irrespective of the level of prestige of the doctoral department of origin. In fact, graduates from the most prestigious departments were most likely to report that race had no impact on their career mobility. Such changes must be placed within the context of larger societal changes. Many of the structural changes were largely due to the implementation of affirmative-action policies and programs in the early 1960s. Affirmative-action programs exerted pressures on the gatekeepers by instructing them to play an active role in identifying the best qualified minority applications; if they ignored this directive, then they, rather than the minorities, would have to pay a price (Blalock 1982). In their study of African American chemists, Young and Young (1976) concluded that business, industry, and educational institutions changed their patterns of hiring minorities solely because of affirmative-action requirements; the employing sectors were threatened with the loss of federal contracts if equal-opportunity requirements were not met. Finally, evidence strongly suggests that not only must minorities acquire skills to make them more competitive in the workplace, but the government must also play a strong role in implementing and enforcing legislation to ensure equal opportunity in the workplace for all its citizens. Workplace discrimination must be aggressively monitored and punished.

References Blalock, H. M., Jr. 1982. Race and Ethnic Relations. Englewood Cliffs, NJ: Prentice-Hall. Franklin, J. H. 1966. The dilemma of the American Negro scholar. In H. Hill, ed., Soon, One Morning: New Writings by American Negroes (1940–1962), 60–76. New York: Alfred A. Knopf. Haber, L. 1970. Black Pioneers of Science and Invention. New York: Harcourt, Brace, and World. Klein, A. S. 1971. The Hidden Contributions of Black Scientists and Inventors in America. New York: Doubleday. Lillie, F. R. 1942. Obituary. Science 95: 10–11. Manning, K. R. 1983. The Black Apollo of Science: The Life of Ernest Everett Just. New York: Oxford University Press. Pearson, W., Jr. 1985. Black Scientists, White Society, and Colorless Science: A Study of Universalism in American Science. Millwood, NY: Associated Faculty Press. Young, H. A., and B. A. Young. 1976. Black doctorates: Myth v. reality. Chemical Technology 6: 296–299.

ronni denes

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ronni denes

Gaining Access A Research and Policy Agenda As America moves toward the twenty-first century, the word minority is rapidly becoming an anachronism. African Americans, Latinos and American Indians, already more than 28 percent of the college-age population and 33 percent of the birthrate, will, by decade’s end, be one-third of the nation’s work force and the majority population in 53 of its largest cities. Vast and growing, these groups still comprise less than 6 percent of the science and engineering labor force, but a staggering 31 percent of people living in poverty. In a nation where technology-based industry has been the mainstay of economic growth for more than half a century, the inability to build or replenish technical talent from the ranks of all our resident populations is untenable on several counts. Perhaps most obvious, by failing to qualify a third of the American people for the nation’s fastest growing and highest paying careers, we’re creating an impenetrable barrier between haves and have nots, a barrier defined by access to higher education and clearly erected along lines of race and ethnicity. Much less discussed, but certainly no less damaging, the stratification our country now maintains denies industry and academia the intelligence, creativity, and potential contributions of an enormous share of our human resources. The NACME Research and Policy Conference on Minorities in Science, Engineering and Mathematics was convened, with support from the Alfred P. Sloan Foundation, to assess what is known about increasing minority participation in technical fields and—with that knowledge catalogued for the first time in a single place— to establish both research and policy agendas for the coming decade. Gathering the nation’s leading scholars, researchers, educators, and public and private sector policymakers, the conference was action-oriented by design. That is, the conferees were committed, after identifying the known, to highlighting gaps in knowledge that would fuel future research and gaps in action that would shape effective policies. The ultimate goal: to accelerate the production of minority scientists, engineers, and mathematicians, feeding the growing technical work force from the full potential of the nation’s population.

Building a Research Agenda Any broad exploration of human behavior poses a set of possibilities for learning that is infinite. In examining the seamless web of cultural and educational factors that impact participation and achievement in the sciences—with the weave controlled by such variables as ethnicity, gender, geographic location, and socioeconomic sta-

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tus—the number of research questions that can be addressed is also limitless. But, at a time of scarce funding for research and with a striking need for action that can reshape outcomes now, the consensus of the assembled community was that we must, of necessity, be frugal in selecting issues for study and that we must focus on those with the greatest potential to drive broad, systemic change. At the same time, we must look to the funding community, whether it be government or private sector, to assure that existing data and future data collection are of optimal use to the research community. Disaggregate All Collected Data The wealth of statistical information that currently resides in the myriad data banks of government agencies and other institutions has neither been disaggregated nor standardized to be easily accessible to researchers, policy analysts, and practitioners. Whether it is because the information is unavailable or simply unpublished, obtaining comparable data disaggregated by race, ethnicity, gender, socioeconomic status, discipline, geography, and so on remains a significant challenge. Despite common government funding, lack of uniform collection parameters and definitions among the Bureau of the Census, Bureau of Labor Statistics, National Center for Education Statistics, and National Science Foundation, to name just a few, yields statistics that often cannot be cross-referenced, are inconsistent, and sometimes are even contradictory. That the attempt to develop a coherent set of data on degree attainment by scientific discipline and by ethnicity continues to represent a challenge to the research community is unacceptable, given current needs. Looking forward, there are compelling reasons to assure that minorities are neither perceived nor studied as homogeneous groups. In examining factors that lead to the attainment of SEM degrees, the great variation in behavior and motivation among women from different racial/ethnic groups, between men and women of the same racial/ethnic group, among Latinos of differing national origins, and between African Americans from the inner cities and the rural South lends a sense of urgency to disaggregate both quantitative and qualitative information. And, with the prevalence of electronic distribution rapidly reducing the cost of dissemination, there is no longer reason to limit publication to gross national aggregated statistics. Establish the Cost of Underrepresentation Mobilizing the intellectual, physical, and financial resources to create broad social change is a massive undertaking that, to be successful, must be driven by critical societal need. In addressing minority participation in science, engineering, and mathematics, there was a time when the research and policy communities needed only to cite moral issues—fairness, equity, equal opportunity, whatever the nomenclature—to underpin both study and advocacy. In the late 1980s, as the momentum of the civil rights movement waned, however, the case for action needed restating. The new impetus centered on human resources: dire predictions of impending work force shortages in the very fields that were maintaining the nation’s toehold as an

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economic superpower. But, neither social justice nor work force need made the irresistibly compelling case represented by the extraordinary cost of embargoing an entire employment sector from an enormous segment of the population. What is the cost of underrepresentation in the sciences? What portion of the gross national product, for example, is driven by the labor force skilled in science, engineering, and mathematics? What might be the economic gains of full access employment? What would be the ripple effect on income tax revenues? On the reduction of welfare rolls? On the state level, could we model the impact of providing full scholarships in SEM fields against potential reductions in health care costs or the explosive cost of incarceration, which now compete vigorously with higher education for funding? If we are to advocate for appropriate education funding, we must develop the data to shift the discussion from what educating the disenfranchised will cost to what America pays today to maintain the status quo. Identify the Institutional Practices that Promote Access For more than a quarter of a century, interventions designed to promote minority access to SEM careers have centered largely on fixing the individual. Embracing this student deficient model has encouraged painstaking, often microscopic examination of the disadvantages that underrepresented young people carry with them through the education system. The programs developed as a result—heavy with tutoring, self-esteem building, basic repair techniques—provide services designed to teach the student to survive in a basically hostile set of institutions. Two and a half decades with only limited success, however, indicate that the research community must turn from questions of why children fail to explore why we as teachers, as schools, as policymaking communities continue to fail a vast population of students, and more importantly, to identify the vital lessons to be gained from the small subset that succeeds. There is no question that at every educational level, from preschool through the Ph.D., inequities in the learning experience cause underrepresented students to enter institutions with greater gaps in formal instruction than their more affluent peers. Increasingly, however, the interventions and institutions posting the most significant successes demonstrate that content is readily recovered by students in nurturing educational environments. In fact, confounding traditional efforts at replication, in the most effective situations it is the environment itself, complete with staff attitudes and competencies, rather than a simple set of services, that must be reproduced. The challenge for researchers is to unravel the multivariate equation, the balance of curriculum, pedagogy, funding, and the too-often ignored impact of teacher expectation that together support academic achievement. Within this context, excellent untapped opportunities reside in the program community, where rigorous assessment could provide an efficient workshop for identifying potential success factors for institutional and systemic change. Define Factors that Shape Minority Attrition and Persistence in Undergraduate and Graduate SEM Programs While the magnitude of attrition from science, mathematics, and engineering programs is high for all students, for students from the underrepresented communities

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it remains unconscionable. Institution-by-institution analyses indicate persistent patterns of differential retention rates between minority students and others that should not be predictable by race, ethnicity, or gender. Although a significant body of descriptive work offers insight into field switching and stopping out versus dropping out—leaving higher education altogether— the institutional research has largely been short-term and problem-specific and has not addressed the broad spectrum of issues unique to minority students in rigorous science-based majors. Truly understanding the interplay of disparate attrition factors—for example, financial resources, ethnic and gender isolation, campus climate, relevant curriculum, effective pedagogy, peer and faculty expectations, absence of role models, precollege preparation, reasons for choice of major, academic and social support programs, the culture of mathematics and science, university commitment to diversity, practical workplace experience—demands an investment in long-term longitudinal panel surveys that deliver hard-edged, quantifiable insights, illuminated by qualitative studies of human interaction on campus. Admittedly both time consuming and expensive, long-term, systematic analysis holds greater promise than any other methodology for understanding how to conserve and expand human resources in all SEM disciplines and across all racial/ethnic groups. Examine the Impact of Changing State Policies on Access to SEM Careers Despite vocal federal advocacy in recent years for increased diversity in science-based careers, as well as for broad scientific literacy for all Americans, it is policy action at the state level that has the greatest impact on the content and conduct of public education. With some 16,000 local school districts in the nation—many vigorously resisting the adoption of national education standards, others grappling with various stages of math/science education reform—it is state leadership, not federal, that establishes the level of the bar for curriculum compliance and school certification. Stateby-state analysis of student educational achievements paired with a comprehensive review of regulatory practices would yield a compelling case to argue before those legislators who’ve consistently placed the bar too low, or allowed its height to vary in accordance with the ethnic or economic makeup of individual districts. Further along in the educational pipeline, the overwhelming choice of public institutions by minority students magnifies the impact of tuition and financial aid allocations made in the state house. To the detriment of minority populations, however, allocation decisions increasingly are based neither upon educational need nor sound work force development planning. Instead, they are based on the simple arithmetic of misplaced priorities. Throughout the country, higher education is in direct competition for funding with the burgeoning corrections industry and soaring health care costs. With state budgets showing dollar-for-dollar shifts from education to prisons, enlightening state policy requires that researchers measure the cost of such action not just in access to higher education and impact on production of valuable SEM graduates. They must also project the long-term consequences of missed investment opportunities for the state’s high-tech work force resources, and ironically, on eventual budget increases for corrections, welfare, and health care systems.

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Examine the Career Paths of Minorities in the SEM Work Force Of all the issues explored by the conference attendees, none was based on less empirical data, none offered fewer opportunities to cite existing research, none was more steeped in anecdotal reporting than the examination of minority progress and achievement in the SEM work force. Even in engineering, a field in which companies have spent more than two decades pooling their resources to produce a growing cadre of minority professionals, there is virtually no industry-wide study that tracks the career progress of minority engineers and compares it to the experiences of their nonminority peers. While individual corporations may have limited sets of proprietary data—personnel records, exit interviews, employee surveys—the opportunity to determine the nation’s collective achievement in developing a diverse engineering work force, or simply to base new programs on the firmament of best practices, literally does not exist. Surely, if we are to increase access to SEM careers, a broad understanding of what has happened to those minority professionals already in the workplace, based on large-scale systematic surveys in both industry and academia, is crucial. Given the dearth of existing data, studies must be designed to meet three critical information needs. First, we must establish a baseline, an understanding of the current status of minority professionals in SEM careers and how their experiences compare with those of nonminorities as they move through the work force. By identifying trends in educational attainment, employment offers, time to promotion or tenure, salary growth, patents or honors earned, publications and presentations, job satisfaction and career change, and disaggregating the data by gender and ethnicity, we can begin to model the realities of career development and growth for minorities in the SEM community. At the same time, researchers must explore the tremendous complexities of workplace interactions across barriers of race and gender. How do differences or similarities in ethnicity and gender impact on reporting relationships: choice of assignments, access to advanced training, opportunities for high-profile exposure, support for professional and technical activities? And what is the larger impact of organizational commitment to diversity on such broad issues as minority success or attrition? Finally, workplace research should yield concrete recommendations for precollegiate academic preparation, undergraduate education, university-to-work/graduate school transitions, and career development in corporate, government, and academic settings. Done well, workplace studies can provide otherwise unavailable keys to retention in SEM careers as well as replicable practices that lead to extraordinary achievement.

Establishing Effective Policy The importance of aggressive leadership in increasing the magnitude and pace of access cannot be overstated. While researchers continue to grapple with persistent questions of how and why, cohorts of young people, already in the pipeline, will be lost to the sciences without swift and decisive action. We simply cannot afford to

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wait for all the definitive answers. Fortunately, in the body of extant knowledge, there are sufficient solutions to warrant policy recommendations for the full community of stakeholders: federal, state, and local governments, university and precollege educators, and business and community leaders. It is important to note that, in developing policy directions that would yield swift, systemic change, there was purposefully little attention paid by the conferees to ease of implementation. Rather, what follows is a set of “imposable solutions.” These are policy shifts that, if embraced and funded at the appropriate leadership levels, have the power to create rapid, dramatic growth in both the academic potential of our students and the diversity of our technical work force. Provide High Quality Science and Mathematics Education to All Students In today’s economy, a high quality education in mathematics and science can no longer be considered a luxury reserved for youngsters identified as talented. There is no science gene. Rather, there is a huge and growing demand for students who graduate from high school with mathematics courses through calculus and science through physics—not only as future Ph.D.s but as workers on the modern factory floor, as computer systems operators and applications users, as technicians and machinists. Given a global market that will locate its businesses where it finds the most readily available, highly skilled labor force, this is neither an education issue nor a diversity concern. It is a national human resources priority that must be recognized as such and treated with concomitant urgency. Policy recommendations. • Mandate four years of academic track mathematics and science courses for every student who attends one of our nation’s public high schools. • Embrace national standards in mathematics and science education so that students are provided a relevant, coherent curriculum despite movement between schools or states, and so that teachers understand clearly what is expected of them and of every student. • Enhance the professional development of teachers assuring that they have effective pedagogical strategies for delivering mathematics and science concepts, and the expectation that all students will learn. • Create equitable access to new technologies, establishing basic skills and competencies for all students. • Provide rich, early educational experiences, with an emphasis on reaching those families with the fewest resources. Assure that College is Affordable for All Americans The skyrocketing cost of tuition—with average annual increases running two to three times the rate of inflation—has combined with shifts in financial aid policy at federal, state, and institutional levels to make college less affordable today than at any

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time in our history. Each sector has grappled with economic considerations of its own: the federal government moved the bulk of its financial support from scholarships to loans; the states found funding for corrections and health care by switching the burden of payment for higher education to increased tuition; and the universities—also struggling to raise revenues—began using scarce scholarship dollars as the carrot to leverage cash tuition payments by enrolling more affluent students. For the poorest segment of the population, which is disproportionately minority, the changes have placed a college education well beyond the aspirations of many students. And in rigorous science-based disciplines, where the time demands of course work make even a 20-hour work week impossible, inadequate funding severely handicaps the retention prospects of those who enroll. Policy recommendations. • Develop a more realistic national financial aid policy that creates access for the poorest students through scholarships and forgivable loans. • Reassess state policies that are driving up tuition at public institutions and restore affordability of public education to the public. • Increase institutional accountability for providing scholarships to the students with the greatest need by tying university support to the human resources goals of funding organizations and government agencies. Develop Institutional Accountability for Access and Retention in Postsecondary Education Minority students are not well served by the vast majority of colleges and universities. At all degree levels, the preponderance of minority SEM graduates continues to be produced by an extremely small fraction of institutions. In engineering, the skewing is so great that 10 percent of all accredited programs produce more than half the minority BSE graduates. The nation’s Historically Black Colleges and Universities (HBCUs) continue to educate a disproportionate share of African Americans, steadily producing about 30 percent of the annual bachelor’s degrees in SEM fields; in physics and mathematics, the figures approach 50 percent. Similarly, the member institutions of the Hispanic Association of Colleges and Universities (HACU) graduate the lion’s share of Latinos, and a small handful of institutions produce the bulk of American Indian graduates. Confounding the obstacles to access is the differential attrition between minority students and others at those schools where they do enroll. Policy recommendations. • Hold the nation’s research institutions accountable for educating a population reflective of the American people by linking their substantial federal, that is, taxpayer, funding to achievements in diversifying their graduating classes and tenure-track faculty. • Leverage corporate, foundation, and government grants to postsecondary institutions by including explicit human resources outcomes among the criteria for evaluating proposals.

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• Track and publicize institutional achievements in attracting and retaining underrepresented minority students by department and program. • Revisit tenure, promotion, and merit award policies to improve faculty incentives for undergraduate teaching, mentoring, and advising that are successful across racial, ethnic, gender, and socioeconomic boundaries. • Build the ranks of minority faculty by providing existing faculty with incentive grants for stimulating the production of minority researchers and Ph.D. candidates. Improve Public Understanding of the Benefits and Availability of Education in the Sciences Children today will grow up to face workplace realities that are remarkably different from the situations encountered by their parents and their teachers. The computer, still a challenge to the vast majority of educators and a mystery even to many younger parents, will be ubiquitous in the twenty-first century workplace. Already, news stories catalog corporations’ desperate searches for new employees adequately educated in mathematics and science. Companies invest billions of dollars annually in retraining existing personnel. And CEOs identify the availability of a highly skilled work force as a priority concern for the coming decade. All this notwithstanding, the myth persists among parents, teachers, guidance counselors, and school board members that not every child needs mathematics and science through the level of calculus and physics, that they managed to achieve reasonable life situations without such courses and so can their children. In the absence of rigorous course requirements and with teachers and counselors actively discouraging students from enrolling in classes that they perceive as too difficult or unnecessary, young people are making academic decisions that are devastating, not only for their future job prospects but for the growth and development of the nation’s economy. Policy recommendations. • Disseminate information aggressively to local education policymakers, schools, families, and students about the importance and benefits of education in science and mathematics, the availability of attractive career options in SEM disciplines, and the dearth of opportunities for those without advanced science and math courses. • Educate state and local governments to the direct linkage between an available SEM-skilled work force and community economic development. • Stimulate the education reform process by engaging the broad community of stakeholders, publicizing best practices, and leveraging the resources of funding organizations and agencies. Support Programs that Specifically Target Minority Students as Tools for Creating Access To assume that America’s educational playing field is level is naive at best, and more likely disingenuous. Students from families in the top income quartile are 13 times

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more likely to earn a bachelor’s degree by age 24 than students in the bottom income quartile, and minority students are disproportionately poor. They are also disproportionately poorly educated. Only 6 percent of all minority youngsters in the United States—compared with 15 percent of all students—graduate from high school with the four-year sequence of mathematics and science courses to major in any science-based discipline in college. And when this tiny cadre of well-prepared minority students applies to college, they are also disproportionately disadvantaged by reliance on standardized tests that are more highly correlated with parental income and education than with any outcome measures of student achievement. Unless and until this situation resolves, the policy community must maintain as a priority the support of programs that explicitly create access for minority students. Whether they are called affirmative action programs—a term that has been so politicized that its meaning, not preferential treatment but equality of opportunity, has become lost in rhetoric—or renamed for ease of application, targeted support is absolutely crucial for the development of adequate human resources for the nation’s technical work force. Policy recommendations. • Maintain and expand federal funding for programs—precollege through the Ph.D.—that specifically target the development of SEM professionals from the underrepresented minority communities. • Support institutional commitment to diversity by providing federal, foundation, and corporate incentives to institutions that demonstrate increased access and retention of minority students in the SEM pipeline. • Encourage the development and widespread use of alternative assessment techniques that more accurately predict minority student achievement. • Codify and disseminate effective strategies for enrolling and providing financial support to underrepresented students within the emerging legal framework. At the close of the twentieth century, the United States remains a nation deeply divided by race and ethnicity. That there are formidable barriers to the equitable participation of African Americans, Latinos, and American Indians in the nation’s scientific enterprise is neither surprising nor unique to this specific sector of the economy. What is particularly challenging in creating access to the sciences, however, is the strength and complexity of the infrastructure supporting minority exclusion. Compounding the impact of substandard K–12 education and lack of resources for college is the intricate interplay between the social construction of mathematics, science, and engineering and the American socialization processes that are defined by race. What is also specific to the SEM disciplines is the urgency to take down the barriers immediately. With enormous employment growth in the nation’s burgeoning high-tech industries and rapidly expanding minority communities that have been largely excluded from wealth-building opportunities, we are at a critical moment in our economic history. The scientific knowledge and technical achievement that have crowned the nation uncontested leader in the global market have the potential to enrich all our

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nation’s people or to solidify the great gaps between us. To fail at this moment to nurture broad access to the skills that will expand our technology-based economy will jeopardize the standard of living not only for minorities but for all Americans. If, however, we act now, quickly and with bold strokes, to identify the specific impediments to minority participation and to make the hard policy decisions to address them, the possibility exists today to catalyze a significant infusion of minority talent into the technical mainstream, to build permanent pathways to science, engineering, and mathematics careers, and to spur unprecedented economic development into the new millennium.

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Index

ability-based grouping, controversial effects of, 159, 175 academic achievement adolescent factors, 90–91, 99–116, 126–128, 153–158 college factors, 193–205 diversity within same school level, 114–115 doctorate faculty as key to, 4, 301–303 early childhood factors, 50, 52–56, 58 ethnic gains in, 54–55, 99–103, 179–180 high school factors, 177, 179–183, 193–196 learning opportunities through, 54–55, 69– 70, 213 measurement methods for, 29–32, 66–69, 156, 158 minority discrepancies in, 53, 179, 195–196, 213 transition to industrial careers, 282, 284–287, 307–310, 318 Academic Apartheid, 3 academic careers minority trends in. See minority faculty policy perspectives, 274–277 research strategies for, 269–275 academic institutions accountability recommendations, 275, 320– 321 categorization for research, 240–241, 250– 253, 270–271 field switching correlation, 250–253 school-to-work transition role, 286–287, 318 two- versus four-year, 233, 235–236, 242–244 See also specific type academic rank, minority trends in, 243–244, 276–277, 312

Academy for Educational Development, research by, 137–140 accelerated programs enrollment data, 30, 69–70, 129, 153, 156, 179 instruction strategies for, 160 access, gaining effective policy for, 318–323 research agenda for, 314–318 Access Science program, 82 accountability, of academic institutions, 46, 275, 320–321 adaptive responses, of institutions, 207–211 administrators, minority, 242, 246 admission rates and practices. See college admissions adolescence achievement factors during, 99–116, 127 attitudes about science during, 90–99, 126– 127, 153–156 career aspirations during, 116–121, 144–145, 151, 181–182 developmental markers of, 89–91, 140–144, 151–152, 165 instruction strategies during, 106, 110, 112, 114–116, 160 intervention programs for, 140–146, 163–166 research strategies for, 126–128 advanced placement (AP) courses, 179. See also accelerated programs advisors, in graduate school, 253, 258, 297–299, 302 affirmative action as career mobility factor, 312–313 in graduate schools, 246–247, 258, 276 recent criticism of, 3, 210, 219, 322

325

326 Index affordability, of college education, 222–223, 319–320 African Americans academic performance of. See specific discipline degree trends per profession, 12–27 demographic trends, 9–13 historical perspectives, 3, 36–38 intervention program results, 50–51 scholarship programs for, 3–4 as scientists example biographies, 311–313 research gaps in, 3, 271–272 underrepresentation of, 7–8, 39–41 Alfred P. Sloan Foundation, research funded by, 181, 314 algebra, as gatekeeper, 176, 178, 189 alienation, cultural, career choice and, 196– 197, 251–252 American Association for the Advancement of Science (AAAS) intervention program studies, 49–50, 57 program strategies of, 59, 82, 287 American Chemical Society (ACS), programs of, 82, 284, 287 American Indians academic performance of. See specific discipline degree trends per profession, 12–27 demographic trends, 9–13, 38 historical perspectives, 3, 36–38 intervention program results, 49–51 workforce underrepresentation of, 3, 8, 39–41 American Institute of Physics, data collection by, 14 analytical methods, for career models, 283–284, 317 anecdotal data, 270, 307, 310 annual reports, recommendations for, 75, 86, 128, 275 anxiety. See test anxiety articulation agreements, 209–210 articulation committees, 197 Asian Americans academic performance of. See specific discipline degree trends per profession, 15–27, 196–197 demographic trends, 9–12 assimilation, cultural, strategies for, 37–38 associate degrees, conversion to bachelor degrees, 197–198 astronomy, minority degrees in, 12–13, 15 athletics, as career aspiration, 181, 190

attitudes of adolescents on career choices, 116–121, 144–145, 151, 181–182 influencing factors, 90–99, 126–127, 153– 156 of college faculty, 198, 215–218 of college students, 216–218 attitudinal rating scale, as learning measure, 66, 68, 70 attrition rates college level, 198–200, 204, 213, 215 doctorate level, 301–303, 307 graduate level, 248–253, 275 for high school, 193–194 research strategies for, 316–317 autobiographies, data from, 7–8, 240, 283, 311– 312 bachelor degrees chances for, per family income, 158, 222, 226, 234, 321 completion probabilities, 182–183, 200 conversion trends from associate degrees, 197–198 to doctorate degrees, 28, 39, 271 field-switching patterns of blacks earning, 250–252 trends per discipline, 15–24, 26–27, 33, 157, 183 Banneker scholarship program, 200 behavioral sciences adolescence applications, 140–143, 161 career differences in, 242, 244, 246 beliefs of adolescents on career choices, 181–182 influencing factors, 90–99, 126–128, 153 of educators, in change possibilities, 196 benchmarking, 310 bias. See discrimination bilingualism elementary education strategies, 66, 70–74 as performance factor in middle school, 105, 114, 127 biological sciences, minority degrees in, 17–19, 28, 241, 245, 311–312 births, American trends of, 9 black colleges, 240, 250–253, 270–271, 300– 301, 320 books, as performance factor, 98, 106 Bouchet, Edward, 7–8, 240 brain development, early influences on, 52–53 brain drain, 296 bridge programs, pre-freshman, 202–203, 211

Index budget deficits, as reform issue, 43–44, 46 Bureau of Indian Affairs, relocation program, 38 Bureau of Labor Statistics (BLS), 39, 45 Bureau of the Census, 16, 39 calculators, as performance factor, 111–112 calculus, need for skills in, 161, 319, 321 California Civil Rights initiative, 4 career achievement. See career paths career-awareness activities in early childhood, 78, 82 for graduate students, 306–308 in high school, 181–184 intervention programs as, 49–51, 162–167 in middle school, 119–120, 124–126, 138– 140 in precollege programs, 187–190, 218 research strategies for, 284–287, 321 career choice as adolescent marker, 151–156, 184 developmental theories of, 116–121, 127 divergence theories, 117, 127 education connection between, 282, 284, 298 high school influences on, 154–167, 181–184 middle school influences on, 90–91, 114, 137–138, 143–144, 152–154 minority trends of, 12–27, 162–163 in academia, 242–246, 261 in industry, 281–323 unrealized aspirations, 174–184, 248–249 models for studying, 281–285 research strategies for, 285–287 career choice programs, 123–126 career ladders. See career paths career outcome patterns, 283–287, 301, 318 career paths in academia. See minority faculty individual factors, 285 in industry, 295, 297, 303–304, 310 research strategies for, 295–296, 303, 318 transition influences on, 282, 284–287, 307– 310, 318 career placement programs, 286–287, 296–298 careers academic. See academic careers demographic trends, 9–13, 28, 151–152 diversification status. See diversification; scientists foreign immigrant trends, 33–36, 40, 242– 244, 270, 296 industrial. See industrial careers stereotyping of, 70, 94, 103, 117–120, 128, 182, 282 See also specific discipline

327

Carnegie Corporation reform initiatives of, 139 Task Force on Meeting the Needs of Young Children, 52–53, 84–85 Carver, George Washington, 240 census data, 182, 223 Charlottesville Education Summit, 51 chemistry, minority trends in, 12–15, 241, 246, 282–283, 312 childbearing. See pregnancy children as natural scientists, 56–57, 62, 78–80, 84 resources for. See educational resources Children’s Television Workshop, 56 Civil Rights Movement, impact of, 4, 300–301, 315 Clark Foundation, reform initiatives of, 139 classroom environment intervention program transfer to, 126, 128, 146 as performance factor in middle school, 97, 105–106, 111 clubs. See extracurricular activities cognition adolescent development changes in, 89–90, 115, 140, 175 brain development and, 52–53 construction stages of, 61–62, 79–80, 103 context-specific, 158, 184 curriculum assignment and, 30, 33 out-of-school activities impact on, 56, 141– 143 cognitive style, as performance factor, 104, 116, 127 coherent learning community, 84, 86 collaborative study at college level, 199, 214, 220–221 in graduate education, 254, 263, 273–274 college admissions adaptation processes in, 209–211 graduate level practices, 255, 263, 271 institutional policies for, 197–198, 204–205, 249 rate trends, 182–184, 193–194 college education affordability issues, 222–223, 319–320 articulation agreements for, 209–210 barriers to minorities, 196–199, 202, 204– 205, 207–209 choosing process, factors in, 162–166, 180– 181, 285–286 community-based. See community colleges completion factors, 178, 182–183, 200, 213, 233 degree transfer requirements, 197–198, 209– 211

328 Index college education (continued) demographic trends, 9–11 family income correlation, 158–159, 178, 182–183, 194, 200, 213, 223, 226–227, 232–236, 321 financial aid factors, 183, 198–200, 208, 221–225 financial revenue sources, 226–230 institutional nature of, 196–199, 202, 204– 205, 207–211 instruction strategies for, 199, 210–211, 214– 216, 220–221 reform policies for, 207–211 research strategies for, 205, 212–215 retention factors, 198–205, 248–250 transition from, 247–253, 285–287, 318 transition to, 162–167, 182–183, 202–203 college preparation programs, 29–32, 45, 177, 187–190, 218 Commission of Professionals in Science and Technology, 275 community colleges challenges for, 197–198 financial aid data, 234–235 university articulation agreements with, 209– 210 community institutions as adolescent attitude factor, 97, 140–143 career paths impact of, 181, 184, 218 in education reform, 59, 62–64, 74, 84–86, 141–143 importance to children, 55–57, 84 intervention programs and, 62–64, 74, 80–82, 141–147 as student achievement factor, 50, 53, 158– 159 competition in graduate education, 254, 263, 276 in job performance, 308–310, 313 for science and mathematic skills, 190 computers, personal access differentials to, 58–59, 70, 154–155, 321 as adolescent performance factor, 111–113, 154 computer science aggressive counseling strategies for, 216–218, 296, 321 instruction strategies for, 58, 70 minority degrees in, 22–25, 28–29, 241, 296 Computing Research Association, data collection by, 21 Cooperating Hampton Roads Organizations for Minorities in Engineering (CHROME), 187–189

cooperative learning, during adolescence, 111, 114–116, 160 corporal punishment rates, for high school, 194 corporate leadership diversity advantages in, 309–310 engineering dominance of, 24, 26 Corporate Scholars Program (CSP), 203, 307 corporations leadership trends, 24, 26, 309–310 recruiting practices of, 83, 286–287, 306–310 reform initiatives by, 139–140 counseling programs academic, 138–140, 216–218 vocational, 119–120 counselors for graduate school. See advisors minority, roles of, 200–201 in precollege programs, 188–189 school-to-work transition role, 286 as student performance factor, 161, 181 course selection critical checkpoints for, 162–163, 166–167, 189 in high school, 176, 178–179, 195–196 creativity, 103, 309, 314 critical mass theory, 301 cultural factors in adolescent performance, 105, 126–127, 157–161 ambiguities associated with, 37–38, 70 in career choices, 196–197, 251–253, 287, 316 in college institutions, 196–199, 207–214, 220–221 in education reform, 50–51, 71–73 in graduate school, 251–253, 269–272 organizational, 309–310, 316 in self-identity, 152, 253, 316 as test bias, 180 Curator Kids Club, 142 curricular design college level changes in, 199, 210–211 college preparatory, 29–32, 45, 166–167, 177 general (comprehensive), 177 research strategies for, 166–167 vocational, 160, 162, 177, 189 curricular program selection critical checkpoints for, 162–163, 166–167, 175–177, 189 high school trends, 176, 178–179, 195–196 curriculum assignment into accelerated programs, 69–70, 129, 153, 156, 179 grouping techniques impact on, 159–160, 175, 177

Index influences on, 30, 33, 114–115, 175–177 strategies for, 69–70, 114–116, 138–140 as student achievement factor, 30, 114, 138, 160, 177 data aggregation challenges, 14, 39, 128, 213–214, 315 anecdotal, 270, 307, 310 autobiographical, 7–8, 240, 283, 311–312 census, probabilities from, 182, 223 disaggregation strategies, 269–271, 273, 315 ethnographic, 214, 254, 271, 283 limitations of, 14–39, 44–46, 63, 214, 269– 270 qualitative, 45, 214, 270, 295 strengths of, 44–46 time-to-degree, graduate level, 270–272 updating importance, 75, 86, 128, 275 data collection for career models, 282–285 consistency of, 39, 315 longitudinal on childhood education, 58, 117, 128, 153 on college education, 213–215 on doctorate programs, 295, 297, 302–304 on high school education, 176–177, 181 interpretation perspectives for, 45, 214 by precollege programs, 188–190 training and work experience, 270–274, 295, 304, 310 minority study sources, 14, 16, 21, 24, 37–39, 44–45 decision-making skills, 309–310 demographic trends, American markers of, 9, 152 per education level, 9–16, 25, 28, 45 per professions, 9–13, 28, 151–152 precollege, 29–32 of race and ethnicity, 9–13, 29–32, 314 of women, 9–10, 103 developmental programs, for teachers, 138–140, 145 developmental theories of adolescence, 89–91, 140–144, 151–152, 165 of career choice, 116–121, 127 of cognition, 52–53, 84, 89–90, 115, 140, 175 disabled persons career choice influences, 82, 120 demographic trends, 9–10 diversity task force for, 51–52, 82, 85 discipline rates, for high school, 195

329

discrimination American trends of, 40–41, 156, 165 at doctorate level, 296–298, 312–313 in faculty careers, 243, 246–247, 254, 263 policies to reverse. See affirmative action in schools, 30, 180–181, 194–195, 254, 256, 258 in tests, 180 in workplace, 312–313 divergence theory, of career choice, 117, 127 diversification faculty dimensions of, 239–247 lack of. See underrepresentation policies to promote, 51–52, 318–323 of scientific workforce academic issues with, 276–277 arguments for, 44, 309–310 progress status, 3–4, 300–301, 309 research agenda for, 314–318 student dimensions of, 208–209 within achievement levels, 114–115, 145– 146 university adaptation model for, 207–209 Diversity Seminars, 203 doctorate degrees academic career trends, 10–13, 28, 239–278 admissions practices, 255, 257–258 barriers to, 241, 253–260, 262–263, 311–313 conversion trends from bachelor degrees, 28, 39 critical transition points for, 301–303, 308– 310 employment limitations for, 241–247, 311– 313 employment opportunity limitations for, 295–298, 303–314 financial aid programs, 255–260, 306–307 foreign student trends, 33–36, 242–244, 276 gatekeepers of, 4, 247, 276–277, 313 industrial career trends, 279–323 interpersonal disadvantage factors, 258–259 minority women’s perceptions of, 256–258 overproduction of, as reform issue, 43–44 recruitment strategies, 255, 257–258 reform policies for, 274–277 research strategies for, 295, 297, 303 student transition trends, 247–253, 277, 286– 287 trends per discipline, 10–28, 49–51, 279–323 downsizing, 287, 296 drawings, as learning measure, 66–68 dropout rates engineering school, 199, 204 high school, 193 minimization of. See retention programs

330 Index dropout rates (continued) postsecondary education, 249, 272, 275, 303, 317 See also attrition rates early careers in academia, 239–277 in industry, 279–323 intervention programs for, 285–287, 306– 310 early education entrance points to, 49–60 formalized. See specific level importance of, 62–63 intervention programs for, 49–51, 57–59, 78– 83 reform policy obstacles, 84–86 research strategies for, 38–39, 75 science programs for, 78–83 earning potential as adolescent career choice factor, 119–120 with baccalaureate degree, 158, 222, 226, 234, 321 elite protectionism of, 40–41 international incentives for, 36–37 realized. See income social welfare implications, 223–224 economic benefits of diversification, 315–317, 321–323 of education reform, 43, 126, 321 economic gap, ethnic trends in, 36–37, 40–43, 222–223 educational attainment of high school students, 182–183, 193–196 by median family income, 231–232 See also academic achievement Educational Equity Concepts, programs of, 78, 81–82 educational expectations of high school students, 180–182, 194–196 for students by parents, 70–71, 96, 105 by teachers, 70, 97, 106, 112, 198, 201, 218, 256 educational research data issues. See data; data collection minority experiences focus, 284–287, 295, 297, 303 network models of, 295–296 policy issues affecting, 43–46 a practitioner’s perspective, 42–43 review of existing, 57–58 strategic goals of, 45–46, 262–263, 315–317. See also specific educational level women focus, 257, 269, 295, 297, 303

educational resources ethnic access to, 54–56, 106, 154, 223, 319 financial. See financial aid media as. See media microcomputers as. See computers, personal technology-based, 40–41, 62–63, 70 education reform community reconfiguration model, 59 economic benefits from, 43, 126, 321 environmental influences versus, 50, 52–54 The Interim Report goals for, 51 National Science Foundation programs, 50, 58–59 policy recommendations for, 65, 319–323 per educational level. See specific level school districts/school, 129, 139 state and national, 128–129, 139, 321 progress reports, 29–30, 40, 139–140 second wave strategies, 139–140 Urban Schools Science and Mathematics Program, 137–140 education-to-occupation experiences, 284–287, 295, 303 eighth-grade gatekeeping, 166, 175–177, 189 Einstein, Albert, 308 elementary education demographic trends, 9–10 extended day programs for, 63–64 importance of, 54–55 instruction strategies for, 58, 69–73, 78–83 intervention programs for, 49–51 reform policies for, 71–75, 129 research review, 71–75 student achievement diversity in, 114–115, 193–196 elite protectionism, of earning potential, 40–41 encyclopedias, access differentials to, 106 engineering adolescents’ perceptions about, 90–99, 138 minority careers in academic, 239–278 critical issues of, 301, 306–310 foreign students, 33–36, 40, 244 industrial, 279–323 progress status, 3, 184, 242, 306–310 minority degrees in admission correlation, 219–220 bachelor’s, 26–27, 33, 183–184, 203–204 conversion rates, 28–29, 262 doctorates, 25, 201, 242 research strategies for, 261–262 retention programs for, 202–205, 220–221 transfer agreements for, 209–210 trends in, 24–27, 241–242, 248–249 minority programs for, 203, 219–221, 306–310

Index women represented in, 10, 203–204 See also science, engineering and mathematics Engineering Vanguard Program, 202–203 Engineering Workforce Commission, data collection by, 39, 44 English-language usage as achievement factor, 55, 58, 65, 105 in early bilingual instruction, 71–74 in evaluation procedures, 66, 70 enrichment programs for career-awareness, 162 for middle grades, 138–143, 146 student access to, 69–70, 85, 141 enrollment in accelerated programs, 30, 69–70, 129, 153, 156, 179 as institutional adaptation factor, 210–211 as measurement, 156, 176 in schools, 9–11, 29–32. See also college admissions environmental influences on adolescent achievement, 157–159 on early learning, 50, 52–53, 56–57, 84 See also specific influence equivalency degrees, for high school, 193–194 ERIC studies, review of, 57 ethnicity adolescent attitude correlation, 91–99, 153, 155–156 adolescent performance correlation, 99–116, 126–127, 156–158 ambiguities associated with, 4, 37 demographic trends, 4, 9–13 as field switching factor, 248–253 professional associations for, 287 unrealized aspirations and, 174–184, 248– 249 ethnographies, data from, 214, 254, 271, 283 ethnoracial identity, components of, 152 evaluation procedures for doctorate students, 297, 302–303 for educational reform. See annual reports language factor, 66, 70 for new engineering employees, 308–309 for student performance, 29–32 for summer programs, 66–69 exit interviews, 275, 283 Expanding Your Horizons, 162, 164 expatriate scientists, 36, 40 exploration, as learning, 56, 62, 65, 78 expulsion rates, for high school, 194 extended day programs, strategies for, 63–66, 75 extracurricular activities, during adolescence, 97, 119, 138–139, 162

331

faculty. See teachers failure, as employee’s right, 308 family as adolescent attitude factor, 96–97 career choice impact of, 183–184, 252, 272 importance to children, 55, 82, 84 as performance factor in middle school, 105– 107, 109 as student achievement factor, 50, 52–54, 58, 158–159 See also parents Family Nights, learning program, 72–73 fellowship awards, 3, 256–257, 259–260 field switching factors associated with, 251–253, 276 institutional correlation, 250–252 minority student trends, 213, 249–253 research strategies for, 247–249, 261–262, 317 women’s trends, 249–250 financial aid, college level contributing factors, 3, 183, 198–201, 208, 221–224 distribution data, 222–223, 234–236 for doctorate students, 255–260, 306–307 as graduate degree factor, 255–256, 262, 271, 275–276 policy recommendations, 317, 320, 322 Ford Foundation, research sponsored by, 122– 124, 137–140 friendships, curriculum association, 177 funding, educational. See financial aid; revenue gender adolescent attitude correlation, 92–99, 127, 152–156 adolescent performance correlation, 99–106, 110, 116, 126–128, 153, 156–158 degree trends per profession, 14–16 as field switching factor, 249–250, 253 as graduate school factor, 256–260 research strategies for, 127–128, 257, 285 as selection preference, 4, 144, 213 summer program impact on perception of, 67–68 See also women genealogical linkages, ambiguities associated with, 37 genes, brain development and, 53 geometry, selection differentials for, 178–179 globalization, of labor market, 35–37, 40–41, 43, 319, 322 goal conflict, in adolescence, 117 Goals 2000: Educate America Act, 51–52 Goals 2000 Report, 30–32

332 Index government articulation agreements and, 209–210 education reform role, 43–44, 46, 317, 321 as preschool policy obstacle, 85–86 school revenue from, 228–230. See also tax support grade point average (GPA), as degree predictor, 219–220, 307–308 Graduate Record Exam (GRE), degree correlation, 260, 263 graduate school admissions practices for, 255 data collection recommendations, 270–271, 273–274 financial aid for, 255–256 foreign student enrollment in, 33–36, 40, 243–244, 276 minority students’ perceptions of, 254–256 minority students’ transition to, 247–253, 277, 285–286 minority women’s perceptions of, 256–260 performance variables in, 251–253, 269–272 racial climate as factor, 240–241, 246–247, 250–253, 256, 259–260 reform policies for, 274–277 research strategies for, 269–275, 295, 297, 303 retention rates, 248–250, 256, 262, 270, 275 students’ selection criteria for, 271–272 See also doctorate degrees; master’s degrees graduation rates college level, 211, 248–249, 254 doctorate level, 248–249, 254 high school trends, 182–183, 193–194, 322 gross national product (GNP) competitive labor strategies and, 36, 43, 316 higher education’s share of, 228 grouping practices, 159, 175, 180–181 guidance counselors. See counselors health care for early childhood, 52–53, 59 financing of, 221–222, 316–317 pregnancy-related, 52–53, 84 high school college choice process during, 162–166 course selection for, 176, 178–179, 189, 195– 196 demographic trends, 9–11, 29–33, 45 ethnic students’ transition from, 162–167, 182–183, 202–203 ethnic students’ transition to, 151–162, 177– 179 graduation rate trends, 182–183, 193–194, 232

intervention programs for, 158–159, 162– 167, 249 reform policies for, 178 research strategies for, 166, 181, 183–184 student expectations during, 154–167, 180– 181 student performance factors, 78–79, 179– 180, 193–196 High School and Beyond study, 128, 179, 183 high-track classes. See accelerated programs Hinton, William, 311–312 Hispanic Association of Colleges and Universities (HACU), 320 Hispanics academic performance of. See specific discipline bilingual instruction programs for, 66, 70–74 degree trends per profession, 3, 12–27 demographic trends, 9–13 as heterogeneous group, 36–37 historical perspectives, 3, 36–38 intervention program results, 49–51 workforce underrepresentation of, 3, 8, 39– 41, 241 historically black colleges and universities (HBCUs), 270–271, 301, 320 hormones, brain development and, 53 human capital as career model, 282, 285 college decisions and, 158–159 in global labor market, 35–36, 40–41 human resources policy research and trends, 281, 283–285, 296 as strategic focus, 314–316, 320, 322 Imes, Elmer, 240 immigrants foreign students as, 33–36, 40, 243–244 impact of, 4, 37, 296 incentives. See rewards income as career model variant, 281, 284 college education correlation, 158–159, 178, 182–183, 194, 200, 213, 223, 226–227, 232–236, 321 as early education differential, 56, 62, 85 high school graduation rates per, 232, 322 minority differences, 26, 28, 244–247 out-of-school programs based on, 141–143 individual perspectives, of diversity, 90–96, 153, 258–259, 285 industrial careers barriers to minorities and women, 295–304, 311–313 critical issues for, 301, 306–311

Index gaining access to, recommendations for, 314– 323 models for studying, 279–294 policy perspectives, 311–313, 317–323 research strategies for, 314–323 transition from academia, 282, 284–287, 306–310, 318 infant mortality rates, 52 institutional adaptation, student diversity model, 207–211 institutional policies at college level, 196–199, 202–211 promoting access, 316. See also education reform in public schools, 194–195 institutions. See academic institutions; community institutions instructional strategies as adolescent performance factor, 106, 110– 116, 119, 154, 160 bilingual, 66, 70–74 for high-track classes, 160 interactive, 78, 81–82, 111, 114–116 per educational level. See specific level reform stages for, 138–140, 143, 145, 188–190 intelligence research. See cognition interactive instruction, as performance factor, 111, 114–116 interests, adolescent factors, 153, 155–156, 248–249 The Interim Report, of Task Force on Women, Minorities, and the Handicapped in Science and Technology, 51–52 interpersonal relationships, at graduate level, 254, 258–259, 299–300 intervention programs academic components of, 57–59, 163–164 early career, 285–287, 306–310 effectiveness factors, 50, 121–122, 125, 164– 165 historical perspectives, 49–51, 58, 121–122 interrelatedness of, 57–59, 207–209 per educational level. See specific level post-equity phase of, 274 transfer to classroom settings, 126, 128, 146, 164 interviews data from, 275, 283 for jobs, 286 In Touch with Preschool Science Workshop, 82 isolation, in college students, 198, 254, 258– 259, 299–301 Jackson, Shirley Ann, 8 job-search skills, 286, 302

333

Julian, Percy L., 311–312 junior high school, transition to high school, 151–154 Just, Ernest E., 240, 311–312 Kids Investigating and Discovering Science (KIDS), 65–69 Kinetic City Super Crew, 82–83 knowledge. See cognition laboratories. See research and development labor market career choice association, 184, 193, 284 earnings gap and, 36–37, 40–43, 222–223 education connection to, 282, 284 globalization trends, 35–36, 40–41, 43, 319, 322 language achievement impact of, 53, 55, 58, 70, 74, 105 bilingual. See bilingualism English. See English-language usage in evaluation procedures, 66, 74 Spanish. See Spanish-language usage Latinos. See Hispanics learned helplessness, as performance factor, 104 learning adolescent stages of, 89 brain development and, 52–53, 84 childhood stages of, 61–62, 79–80, 84 environmental factors, 50, 53–57 school factors, 54 life table model, of careers, 281–282, 284 Lillie, Frank R., 312 Lilly Endowment, reform initiatives of, 139 loans. See financial aid locus of control, 104, 284 low-achieving tracks, 163 macro environment, of research issues, 43–44 magazines, children’s programs in, 78, 82–83 marriage, inter-cultural, 37–38 master’s degrees, conversion trends from bachelor’s degrees, 28–29 to doctorate degrees, 262–263 mathematics adolescents’ perceptions about, 90–99, 153– 156 analytical skills for, 54–55, 104–105 attitudes about college faculty role, 215–218 home/societal factors, 96–97, 158–159 individual factors, 90–96, 153 research strategies for, 126–127 school-related factors, 97–99, 154, 159– 162, 180–182

334 Index mathematics (continued) computational skills for, 54, 104 minority degrees in bachelor’s, 19–22 conversion rates, 28 doctorates, 19, 21–22 retention factors, 198–204 transfer agreements for, 209–210 trends in, 4, 15–27, 33, 157, 183, 241 skill assessment tests for, 99–100 student achievement gains in, 54–55, 99– 103, 179–180 student performance differences in, 53, 58, 99–116, 179–180 usefulness perceptions of, 153, 156–157, 167 white male stereotypes in, 70, 94–95, 98, 103, 128 See also science, engineering and mathematics mathematics education college level practices, 197, 215–218 community involvement model, 58–59 course selection differentials, 162–163, 176, 178–179, 189 curriculum assignment influences, 30, 138– 140, 176 in early childhood experiences, 56–58, 62–63 extended day programs for, 64–66 instruction strategies for. See instructional strategies intervention programs for early childhood, 49–51, 58–59, 64–75 middle school, 64–75, 122–126, 137–146 National Geographic Society Kids Network Program, 71–73 reform policies for, 65, 319–323 elementary level, 71, 74–75 middle school, 143–147 summer programs for, 66–69, 75 television programs for, 56 Urban Schools Science and Mathematics Program, 137–140 Mathematics Engineering Science Achievement (MESA), 162 maturity-curve salary studies, 281, 284 media in early childhood education, 56, 59, 62, 82– 83 The Interim Report goals for, 51 intervention programs use of, 165 See also specific media medicine, minority careers in, 4, 199, 251, 282, 312, 317 memory, brain development and, 53

mentoring for engineering students, 307–309 strategies for, 138–140, 143, 199–200, 204 women’s challenges with, 297–299 Mexican Americans, attitudes of, 94, 96, 98, 164 Meyerhoff program, 199–200, 211 micro environment, of research issues, 43–44 Middle Grades School Sate Policy Initiative, 139 middle school critical role of, 90–91, 99, 114, 137, 143, 152–154, 175 instruction strategies for, 111, 114–116 intervention programs for, 64–75, 122–126, 137–146 minority barriers in, 137–138, 143–144 reform policies for, 128–129, 137–140, 144– 147 research strategies for, 126–128 student achievement diversity in, 114–115 student attitude factors, 91–99, 126–127 student performance factors, 99–116, 127– 128 transition to high school, 151–154, 177–179 military careers, 163, 194, 204, 282 minimum competency testing, 175 minorities as academic faculty. See minority faculty academic performance of. See specific discipline degree trends per profession, 12–27, 248 demographic trends, 9–13, 29–32 differences of, 36–38 economic underrepresentation of, 36–41, 44 education-to-occupation experiences, 284– 287, 295, 303 as scientists. See scientists similarities of, 36–38 as students. See minority students See also specific minority Minority Access to Research Careers (MARC), 201 minority engineering programs (MEP), 203, 219–221 minority faculty academic rank factors, 243–244, 276–277, 312 barriers for, 241–247, 253–260, 262–263, 311 college roles of, 199–201, 211 discrimination trends, 243, 246–247, 254, 263 diversification trends, 3–4, 211, 240–241, 276, 312 early-career programs for, 275–276

Index employment opportunity trends, 241–247, 276–277 field switching and, 247–253, 276 foreign immigrants as, 33–36, 242–244, 270, 276 full-time versus part-time, 244–245 graduate school perceptions of, 246, 258 preparation status, 239–278 recognition as scientists, 7–8, 240–241 reform policies for, 274–277, 320–321 research strategies for, 260–261 salary differences, 244–246 same-ethnicity, 160–161 Minority Graduate Fellowship Program (MNSF), 259 minority students academic achievement discrepancies, 53, 69– 73, 179, 195–196 career choice trends, 162–163, 174–184, 248–249 demographic trends, 9–11, 29–32 diversity dimensions of, 207–211 field switching trends of, 213, 247–253 retention factors, 182–183, 193–194, 198– 200, 202–203. See also education reform; intervention programs scientific talent of influences on participation, 91–99, 126– 127 instruction strategies for. See instructional strategies programs to develop, 4, 63–69, 137–140, 203, 219–221 unrealized career factors, 174–184, 248– 249 technology access limitations, 40–41, 62–63, 146–147 transition to and from high school, 151–167, 177–182 transition to careers, 282, 284–287, 306–310, 318 transition to graduate school, 247–253, 277 motivation, as learning factor, 70, 138–140, 156 museums ethnic usage of, 55, 141–142 science programs for children, 55, 80–81, 142 National Action Council for Minorities in Engineering, Inc. (NACME) data collection by, 25–27, 39, 44 outcome goals of, 24, 38–39, 306–307, 314 retention programs sponsored by, 202–203 scholarship program of, 306–307 National Assessment of Career and Occupational Development, 117

335

National Assessment of Educational Progress (NAEP) mathematics results, 92, 97, 99–101 results review, 92, 97, 99–116, 179 science results, 92, 97, 101–103 strategies of, 29, 31–32, 53 National Association for the Education of Young Children (NAEYC), 62 National Association of Elementary School Principals (NAESP), 63–64 National Association of Precollege Directors (NAPD), 187–190 National Association of Secondary School Principals, 140 National Center for Education Statistics (NCES) curriculum study results, 177 data collection by, 16, 39, 45, 284 National Center on Postsecondary Teaching, Learning, and Assessment, 214 National Commission on Excellence in Education, 178 National Consortium for Graduate Engineering Degrees for Minorities, 201 National Council of Teachers of Mathematics (NCTM), 175 National Easter Seal Society, programs of, 82 National Education Goals Panel progress reports, 31–32 risk factors in children per, 51–52 National Education Longitudinal Study (NELS) childhood education findings, 58, 117, 128, 153 high school findings, 176–179, 181 National Geographic Society (NGS) Kids Network Program, 71–73 National Institutes of Health (NIH), research funded by, 3 National Middle School Association, 140 National Research Council Committee on Research in Mathematics, Science and Technology Education, 57, 202 National Science Board Commission on Precollege Education in Mathematics, Science and Technology, 121 National Science Foundation (NSF) data collection by, 39, 44–45, 128, 273–274, 284 doctorate goals of, 276, 296 early childhood programs of, 82 fellowship awards by, 3, 256–257, 259–260, 274 high school programs supported by, 163, 188 reform programs supported by, 50, 58–59, 144

336 Index National Science Teachers Association (NSTA), 57, 175 National Society of Computer Education, 296 National Urban League, programs of, 57, 82 natural history model, of careers, 282–283 natural scientists, children as, 56–57, 62, 78–80, 84 newspapers, access differentials to, 106 numeracy, preschool level programs, 56, 58 nursing, achievement factors, 199–200 nutrition, brain development and, 53, 59, 84 occupational choice. See career choice occupational role structure, 283 Office of Technology Assessment, 122, 276 open-ended questions, as learning measure, 66, 68 Operation SMART, 164 out-of-school programs, 56, 141–143 parents as adolescent performance factor, 96, 105– 106, 109, 158 college decisions and, 158–159, 164, 180, 188–189 educational level of, as factor, 53–55, 106, 116, 322 expectations for students, impact of, 70–71, 96, 105 importance as teachers, 52, 55, 58–59 involvement in learning activities, 69–73, 78, 106, 109, 164 working, impact of, 55, 63, 103, 146 See also family PEACHES Program, 80 peers as adolescent attitude factor, 89, 97–98, 127, 144, 156, 164 as adolescent performance factor, 157–158 as college factor, 184, 189, 199 job support from, 309–310 perceptions, adolescent factors, 90–99, 126–128 performance feedback. See evaluation procedures persistence factor effects of, 189, 214–215, 297 research strategies for, 316–317 personal consumption expenditures, on higher education, 228, 230 personal perspectives, of diversity, 90–96, 153, 258–259, 285 PhDs. See doctorate degrees physical activity, as adolescent factor, 140, 143 physical sciences, minority degrees in, 12–16, 25, 28, 251–252

physics barrier penetration in, 7–8, 10 curriculum assignment influences, 30, 319 minority degrees trends, 12–16, 241 need for skills in, 319–320 pinball model, of careers, 283–285, 301 pipeline model, of careers, 281–282, 284, 304 placement strategies career-associated, 286–287, 296–298 curriculum-associated. See curriculum assignment Playtime Is Science program, 78, 81 policy factors. See government; institutional policies; education reform postdoctoral programs, 287 post-equity phase, of intervention programs, 274 precollege education demographic trends, 29–32, 45 intervention programs, 187–190 retention programs, 202–203 predoctoral funding programs, 271, 275–276 predoctoral intervention programs, 274–277 predominantly Hispanic institutions (RPHIs), 271 predominantly white colleges and universities (PWCUs), 271 pregnancy data on, 9, 52 health care importance, 52–53, 84 inter-cultural, 37 prejudice. See discrimination preschool access differential to, 85–86 diversity promotion policies for, 51–52 instruction strategies for, 56–58, 62–69, 78– 79, 81, 84 intervention programs for, 49–51 reform policy obstacles, 84–86 science programs for, 78–83, 85–86 television programs for, 56, 84 prison incarceration, costs of, 316–317 problem-solving skills, 105, 220, 309–310 process model, of careers, 282 productivity, 309 professional associations data collection by, 14, 39, 44, 284 reform promoted by, 44, 140 school-to-work transition role, 287, 306–310 science programs for children, 82–83 See also specific society professional identity, 283, 285 professions. See careers promotions. See career paths Proposition 187, California’s, 4 proprietary schools, 194, 204, 235–236

Index publications, predoctoral, 271 public awareness strategies, 321 public libraries ethnic usage of, 55 intervention program participation, 78, 83– 84, 146 quality education, recommendations for, 52, 319 Quality Education for Minorities Project (QEM), 52 questionnaires, as program evaluations, 66, 68– 69 race adolescent attitude correlation, 91–99, 126– 127, 153, 155–156 adolescent performance correlation, 99–116, 126–128, 153, 156–158 ambiguities associated with, 37, 259 demographic trends, 9–13 as field switching factor, 248–253 as graduate school factor, 240–241, 246–247, 250–253, 256, 259–260 professional associations for, 287 as selection preference, 4, 213 unrealized aspirations and, 174–184, 248–249 See also specific race racism. See discrimination radio programs, for children, 82–83 Rand Corporation, assimilation study by, 37 reactive responses, of institutions, 208 reading material, as performance factor, 106 recruitment for career placement, 286–287, 296–298 for doctorate programs, 255, 257, 262–263, 270, 276 of women and minorities, 296–298 reform policies. See education reform remedial study programs, 69, 159, 178, 198 reputation, of employee, 308 research. See educational research research and development (R&D) competitive global activities in, 34, 36 degree correlation, 26, 28 The Interim Report goals for, 51 management model for, 301–304, 309–310 staff diversification in, 242, 300–301, 303– 304, 309–310 student transition checkpoints, 308–310 research assistantships (RAs), 258, 270–271 researchers networking models, 295–296 role of, 215–218, 321 research grants, for minorities, 3, 201

337

Resource Centers for Science and Engineering, 50 retention programs, college level, 198–200, 202–205 retention rates college level, 198, 248–250 graduate level, 248–250, 256, 262, 270, 275 high school, 182–183, 193–194, 232 reform policies for, 321–323 revenue, higher education refinancing strategies, 221–223 social welfare implications, 223–224, 316– 317 sources of, 226–231, 271, 275, 317, 322 for student aid. See financial aid rewards, strategies for, 167, 181, 190, 303 role conflicts influences on, 103, 118 professional, 283, 285 of women, 9–10, 103, 117 role models career choice influences of, 162, 188, 261 graduate school level, 258, 261, 297 importance of, 70, 103, 126–127 same-ethnicity teachers as, 160–161 sabbatical programs, 287 safety, in science programs, 78 Saturday Academies, 58, 138, 164 scholarship programs. See financial aid Scholastic Achievement Test (SAT) as engineering predictor, 219–220 score differentials in, 155, 180, 196 school-age care (SAC), strategies for, 64–65, 75. See also extended day programs school enrollment demographic trends, 9–11, 29–32 postsecondary. See college admissions school-to-work transitions, key players in, 286– 287, 306–310, 318 science adolescents’ perceptions about, 90–99, 153– 156 attitudes about home/societal factors, 96–97, 158–159 individual factors, 90–96, 153 research strategies for, 126–127 school-related factors, 97–99, 154, 159– 162, 180–182 minority careers in, 3 academic, 239–278 degree transfer agreements for, 209–210 foreign students’ role, 33–36, 40, 242–244, 270, 276 industrial, 279–323

338 Index science (continued) minority degrees in retention factors, 198–204, 248–249 trends in, 15–24, 26–27, 33, 157, 183 student performance differences in, 99, 101– 116, 127–128, 179–180 usefulness perceptions of, 153, 156–157, 167 See also science, engineering and mathematics science, engineering and mathematics (SEM) as adolescent career choice, 152–154, 162– 166 college programs for, 193–236 demographic trends, 9–12, 28, 151–152 education reform goals for, 43, 154–155 major professions of, 12. See also specific discipline stimulating interest in, 50–52, 166–167, 248– 249 underrepresentation of minorities in, 7–9, 39–41, 44–45 science, mathematics, engineering and technical (SMET), diversity promotion policies for, 50–52, 318 science centers, programs of, 55, 80–81, 83 science education adolescent strategies, 111, 115–116, 119, 154 community involvement model, 59, 141–143 course selection differentials, 162–163, 176, 178–179, 189 curriculum assignment influences, 30, 138– 140 in early childhood experiences, 56–58, 62– 63, 78–82 extended day programs for, 64–66 instruction strategies for. See instructional strategies intervention programs for early childhood, 49–51, 58–59, 78–83 middle school, 64–75, 122–126, 141–143, 143–146 National Geographic Society Kids Network Program, 71–73 precollege continuation of, 188–190 process skills for, 78–80 reform policies for, 65, 319–323 elementary level, 71, 74–75 middle school, 143–147 summer programs for, 142 television programs for, 56 Urban Schools Science and Mathematics Program, 137–140 Science in Summer program, 83 Science Linkages in the Community (SLIC), 59, 82

Science Resources Studies (SRS), data collection by, 44–45 scientists children as natural, 56–57, 62, 78–80, 84 diversity taskforce for, 51–52 double-life, 296 expatriate, 36, 40 minorities as barriers to, 295–304, 311–313 challenges for, 7–8, 240, 242 research strategies for, 271–272, 295–296, 303–304, 314–318 resistance to acceptance of, 3, 51–52, 300– 301 underrepresentation of, 8–9, 28, 36–41, 44 women as barriers to, 51–52, 203–204, 295–304 demographic trends, 9–10, 103 research strategies for, 257, 269–271, 295– 296, 303–304 underrepresentation of, 8, 44, 239–247, 256 secondary education demographic trends, 9–11, 29–33, 45 learning activities specific to, 78–79 See also specific level second chances, opportunities for, 144 segregation as college factor, 200, 254, 258–259 in public school system, 195 in workplace, 312–313 self-concept, during adolescence, 103, 118, 126–127, 157 self-confidence as adolescent attitude factor, 93–96, 155–156 as career choice factor, 120, 297–298 in graduate students, 297–299 as performance factor in middle school, 103, 146 self-esteem curriculum association, 177 programs to improve, 138–139, 204, 214 self-exploration, opportunities for, 141–143 self-identity adolescent establishment of, 141–143, 152, 165 through career choice, 282–283 self-perception as adolescent attitude factor, 94–96, 126 as learning factor, 70 sensory experiences, brain development and, 53 set-asides, 276 Seven Sisters colleges, 271 sexism, at graduate level, 259. See also gender Shockley, William, 8

Index significant others, during adolescence, 97 social biography, 282 social environment as adolescent attitude factor, 96–97, 127–128, 143 brain development and, 53 career choice impact of, 181, 183–184, 298 college level strategies for, 207–211, 213–214 lack of. See isolation as performance factor in adolescence, 89, 105–107, 127–128, 160–161 in early childhood, 50, 53–54, 56–58 promoting access through, 283, 285, 316 socialization, professional, 283, 285, 316 social sciences career differences in, 242, 244, 246 research strategies for, 295–296, 303 social welfare, resource reallocation and, 223– 224, 316–317 socioeconomic status (SES) adolescent performance and, 100, 105–106, 127–128, 153–159 college attendance correlation, 194, 213, 222–223 curriculum placement and, 177 as self-identity marker, 152, 165, 269–270 Southeastern Consortium for Minorities in Engineering (SECME), 138, 187–190 Spanish-language usage in early bilingual instruction, 71–74 in evaluation procedures, 66, 70 Spend a Summer with a Scientist Program, 202 Starting Points, 52–53, 56, 84 state-certification requirements, for teachers, 129 Statewide Systemic Initiatives and National Goals, for reform, 129 status attainment model, of careers, 282 stereotypes during adolescence, 94–95, 98, 103, 117– 118, 182 for careers, 181–182, 203, 251, 298 race-related, 70, 94–95, 98, 103, 204 sex-related, 94–95, 98, 103, 182, 203–204 stop-out rates, in graduate programs, 270–271 strategic responses, of institutions, 208 stress, brain development and, 53 strong-coupling hypothesis, 284 Strong Families, Strong Schools, 55–56 student body composition, impact of, 159, 254, 258, 301 student performance. See academic achievement; evaluation procedures study habits, as performance factor, 106, 108

339

summer programs campus-based, 66–69, 75, 122 early childhood strategies, 56, 59, 62, 66, 83 middle school strategies, 122, 138–140, 142 postgraduate transition, 286 precollege strategies, 188, 202–203, 219–220 Super Science Saturday, 82 support groups, for college students, 183, 189 survival model, of careers, 281–282, 284 suspension rates, for high school, 194 Task Force on Women, Minorities, and the Handicapped in Science and Technology, 51–52 tax support for higher education, 221–226, 316 as student achievement factor, 154 teacher logs, for summer programs, 66, 68–69 teacher qualifications developmental programs for, 138–140, 145 as differential factor, 70, 75, 78, 160 recommendations for, 129, 211, 218, 319 teachers as adolescent performance factor, 97–98, 106, 112, 160–161 beliefs in ethnicity, 30, 112, 145–146, 160– 161, 220 college level, 198–201, 210–211, 215–218, 258 effectiveness requirements, 160, 319 expectations of students, 70, 97, 106, 112, 198, 201, 218, 256 graduate school relationships and, 254, 258– 259 importance of, 54, 75, 218, 272 intervention program participation by, 66, 69, 188 minority. See minority faculty parents as. See parents as researchers, 215–218, 321 school-to-work transition role, 286 teaching assistantships (TAs), 258, 270–271 technology access differentials to, 40–41, 62–63, 70 in early childhood education, 62–63 middle school applications of, 146–147 technology centers, programs of, 80–81 telecommunications, in childhood education, 62, 71–73 television, educational impact of, 56, 84, 106, 108 test anxiety, as performance factor, 103, 127 textbooks, as adolescent attitude factor, 98 Third International Mathematics and Science Study, 30–31

340 Index three-dimensional movement, in careers, 283– 285, 301 time-to-degree data, in graduate programs, 270– 272 tracking strategies for athletes, 181, 190 for college preparatory courses, 162–163 performance-based, 114–115, 144–145, 159– 160 traditionally black institutions (TBIs), 240, 250– 253 traditionally white institutions (TWIs), 241, 246–247, 250–252 traineeships, for doctoral students, 275–276 transfer centers, 198 transition points per educational level. See specific level school-to-work, 286–287, 306–310, 318 Treisman lab, 199, 211, 214 tuition fees, 221–223 Turner, Charles H., 311 tutoring, strategies for, 138–140, 258 underrepresentation alleviation arguments, 44–45 defined, 8–12, 28 economic costs of, 315–316 intervention programs for, 49–51 in scientific workforce. See scientists unsupervised children, risks for, 63–64 urbanization trends, of minorities, 38 Urban Schools Science and Mathematics Program (USSAMP), 137–140 U.S. Equal Employment Opportunity Commission, 137–140 usefulness, perceptions of, 153, 156–157, 167

values, transmission of, 157, 181 variables, in career models, 282 vocational education programs, 160, 162, 177, 189 vocational guidance programs, 119–120, 189 vouchers, for college education, 200 W. K. Kellogg Foundation, reform initiatives of, 139 white Americans, demographic trends, 8–12 white-collar professionals, 282 white male stereotypes, 70, 94–95, 98, 103, 128 women academic career trends, 242–243, 245–247 career choices during adolescence, 118, 153, 180 college performance factors, 201, 203–204 degree trends per profession, 14–16, 18–24, 26–27 in doctorate programs, 256–259, 296–297 education reform programs for, 137–140 fellowship awards to, 259–260 field-switching patterns of, 249–250 role conflicts, 9–10, 103, 258, 297 salary differentials for, 245–246 as scientists. See scientists women’s colleges, 271, 304 Women’s International Technical Institute, 304 work, models for studying, 281–285 workforce, scientific. See careers working students, 162 workplace discrimination, 312–313 workshops, for science education, 82–83 Young Scholars Project, 163 Young Scientist program, 65 YouthALIVE, 141–142