Empirical Research in Teaching and Learning: Contributions from Social Psychology (Blackwell Claremont Applied Social Psychology Series)

Empirical Research in Teaching and Learning

Empirical Research in Teaching and Learning: Contributions from Social Psychology Edited by Debra Mashek & Elizabeth Yost Hammer © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33719-8

Empirical Research in Teaching and Learning Contributions from Social Psychology

Edited by

Debra Mashek and Elizabeth Yost Hammer

This edition first published 2011 Ó 2011 Blackwell Publishing Ltd Blackwell Publishing was acquired by John Wiley & Sons in February 2007. Blackwells publishing program has been merged with Wileys global Scientific, Technical, and Medical business to form Wiley-Blackwell. Registered Office John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom Editorial Offices 350 Main Street, Malden, MA 02148-5020, USA 9600 Garsington Road, Oxford, OX4 2DQ, UK The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK For details of our global editorial offices, for customer services, and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com/wiley-blackwell. The right of Debra Mashek and Elizabeth Yost Hammer to be identified as the authors of the editorial material in this work has been asserted in accordance with the UK Copyright, Designs and Patents Act 1988. 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, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor mentioned in this book. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the understanding that the publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional should be sought. Library of Congress Cataloging-in-Publication Data Empirical research in teaching and learning : contributions from social psychology / [edited by] Debra Mashek, Elizabeth Yost Hammer. p. cm. – (Blackwell/claremont applied social psychology series ; 3) Includes bibliographical references and index. ISBN 978-1-4443-3719-8 1. Social psychology–Study and teaching. I. Mashek, Debra J. II. Hammer, Elizabeth Yost. HM1033.E534 2011 302.071’1–dc22 2010042202

A catalogue record for this book is available from the British Library. This book is published in the following electronic formats: eBook 9781444395310; Wiley Online Library 9781444395341; ePub 9781444395334 Set in 10/12.5pt, Minon by Thomson Digital, Noida, India 1

2011

Contents

Contributors

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Preface Debra Mashek & Elizabeth Yost Hammer

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How Can Social Psychology Galvanize Teaching and Learning? Regan A. R. Gurung & Kathleen C. Burns

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A Social Look at Student–Instructor Interactions Janie H. Wilson, Karen Z. Naufel, and Amy A. Hackney

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Self-Construal, Culture and Diversity in Higher Education Shelva Paulse Hurley and Eric Alexander Hurley

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Unintentional Prejudice and Social Psychology’s Lessons for Cross-Racial Teaching Elliott D. Hammer

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Teaching in Ways that Support Students’ Autonomy Johnmarshall Reeve

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Achievement is an Attitude: The Importance of Help-Seeking Attitudes when Predicting Academic Achievement Jessica Clevering, Shelley DeFord, Tasia Yamamura, and Debra Mashek

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Applying the Science of Learning to the Art of Teaching Diane F. Halpern and Clayton L. Stephenson

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Contents Which Strategies Best Enhance Teaching and Learning in Higher Education? John Hattie

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Understanding Faculty Reluctance to Assess Teaching and Learning: A Social Psychological Perspective 143 Dana S. Dunn, Maureen A. McCarthy, Suzanne C. Baker, Jane S. Halonen, and Stacy Boyer Applying Social Psychology in the College Classroom: Teachers and Learners Need (Your) Scholarship Randolph A. Smith

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Contributors

Suzanne C. Baker, James Madison University Stacy Boyer, Moravian College Kathleen C. Burns, University of Wisconsin-Green Bay Jessica Clevering, Dordt College Shelley DeFord, Harvey Mudd College Dana S. Dunn, Moravian College Regan A. R. Gurung, University of Wisconsin-Green Bay Amy A. Hackney, Georgia Southern University Jane S. Halonen, University of West Florida Diane F. Halpern, Claremont McKenna College Elliott D. Hammer, Xavier University of Louisiana John Hattie, University of Auckland Eric Alexander Hurley, Pomona College Shelva Paulse Hurley, Claremont Graduate University Debra Mashek, Harvey Mudd College Maureen A. McCarthy, Kennesaw State University Karen Z. Naufel, Georgia Southern University Johnmarshall Reeve, Korea University

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Randolph A. Smith, Lamar University Clayton L. Stephenson, Claremont Graduate University Janie H. Wilson, Georgia Southern University Tasia Yamamura, Scripps College Elizabeth Yost Hammer, Xavier University of Louisiana

Preface Debra Mashek & Elizabeth Yost Hammer

Dr Stuart Oskamp envisioned the Claremont Symposium on Applied Social Psychology as a venue for scholars to examine “topics crucial to our understanding of human relationships and the building of a healthy, diverse society” (cgu.edu/ pages/4513.asp). Since its inception in 1986, the Symposium has time and again achieved this aim by interrogating timely issues such as extremism, the integration of work and family, community engagement, stigma and prejudice, psychological aspects of the legal system, the prevention of risk behaviors, intimate relationships, and cross-cultural relating. Feeling that research in social psychology offers many lessons for higher education, we sought to continue this tradition by organizing the 2009 Claremont Symposium around issues of central importance to each of our colleagues and all of our students: teaching and learning. Once the conference theme was selected, it was clear that a collaboration was in order. To organize a high-quality and topical conference, the organizers should have a foot in both the social psychology and the teaching and learning worlds. Luckily, over several days of grading AP Psychology exams (and a few beers), we made a connection and the planning began. Our collaboration includes two social psychologists. We are from different parts of the country, different types of institutions, and have focused our careers on different content matter. However, we have a similar passion for our students and classroom experiences and we both recognize the value of viewing education in social psychological terms. Debra Mashek specializes in close relationships and the application of close relationship theory to issues of community connectedness and community engagement. She is an Assistant Professor of Psychology at Harvey Mudd College, a liberal arts college in Claremont, CA, and has received national recognition for her teaching. Elizabeth Yost Hammer has spent her career exploring issues related to teaching. She is currently a Kellogg Professor in teaching and serves as the Director of the Center for the Advancement of Teaching at Xavier University of Louisiana, the only historically Black and Catholic university in the country.

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Our Vision of the Volume In our initial conversations about the array of topics we would like represented in the symposium and ultimately this volume, it became clear we both felt strongly that social psychology offers more than merely interesting insights into the form and function of teaching and learning. Indeed, social psychology offers concrete tools for enhancing teaching and learning. Thus, we wanted contributors to both the Symposium and this volume to draw upon empirical research and empirically-based theories from social psychology to inform the scholarship of teaching and learning. Chapters begin by providing an accessible theoretical grounding in the social psychological principles most relevant to the authors’ area of expertise. Chapters then describe how this theoretical lens informs our understanding of teaching and learning (i.e., demonstrating how we can see these processes through a social psychological frame). In addition to providing specific empirical evidence drawn from teaching and learning contexts, chapters provide concrete strategies for translating this evidence “on the ground.” Importantly, we see this volume as a resource for all who are interested in enhancing teaching and learning in the higher education setting. The emphasis on applyingsocial psychology—in conjunction witha solid empiricalapproach—makes this volume relevant to scholarly teachers from all disciplines. Although all chapters focus on social psychological questions, the answers to these questions are relevant to all classrooms. Likewise, we anticipate the people who staff centers associated with facultydevelopment initiatives designedtoadvance the art and science ofteaching will find value in the empirically-validated tools offered in these chapters.

Content Contributors Social psychology examines the many ways we are influenced by both social and non-social contexts. As the table of contents of any social psychology textbook reveals, the topics within social psychology are vast and varied, ranging from selfperception to other-perception, helping to hurting, and self-identity to group identity. Rather than attempt an exhaustive survey of the many ways social psychological theories bear on teaching and learning, the contributors we invited crafted chapters highlighting what is possible in this domain. We knew that we needed someone to set the context of the volume—to establish the usefulness of researching and applying social psychology in the classroom. Regan Gurung quickly came to mind. He recently co-authored a book, Optimizing teaching and learning: Practicing pedagogical research, directly calling for an increase in the scholarship of teaching in learning. We knew his voice would make the case for our theme.

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When considering social psychology in the classroom, one of the first things that comes to mind is the student–teacher relationship and classroom interactions. Because of her work on immediacy behaviors, Janie Wilson was a natural for our theme. She is one of the first scholars to do empirical, classroom-specific research on issues such as touching (in an appropriate context, of course) and first impressions of faculty formed via a pre-semester email. The study of stereotyping and prejudice is one of the most enduring areas of social psychology. Obviously the applications to higher education are vast. We recognized that Shelva Hurley would make a significant contribution given her work on the impact of different cultural expectations on the educational experiences of students at the institutional level. In contrast to Hurley’s consideration of students of color at majority White institutions, Elliott Hammer examines crossracial teaching from a unique perspective. He—a White male teacher at a predominantly Black university—surveyed students about their experiences with faculty and applied many of the concepts from the stereotyping and prejudice literature to his findings. Questions about the interactions between people sit at the heart of social psychology. Johnmarshall Reeve asks and answers such questions in the context of interpersonal motivation. In particular, how do teachers motivate students? He draws from self-determination theory, and his extensive work with educators at all levels, to advocate for autonomy supportive classrooms. As with the study of stereotyping and interpersonal interaction, attitudes likewise are a cornerstone of social psychology. Thus, we wanted to include some research examining the influence of attitudes on behaviors in higher education settings. Jessica Clevering’s work on help-seeking seemed like a perfect fit. She found that attitudes about asking for help, more so than help-seeking behaviors, are related to academic achievement. Although not a social psychologist, Diane Halpern is a recognized expert on learning and teaching. She has been a leader in expanding the scholarship of teaching and learning, and applying theories and tools from our content disciplines to student learning and educational experiences. We knew her contributions would both energize the discussion and offer a prime example of how to “do” the scholarship of teaching and learning. Building on his expertise in academic self-concept and quantitative methodologies, John Hattie tackles a fascinating predicament in the teaching and learning literature: evidence exists demonstrating the efficacy of most all educational interventions. Yet, as all students know, not all teachers or forms of instruction are equally effective. With an eye toward pinpointing the elements of effective teaching, Hattie offers a synthesis of over 800 meta-analytic studies to identify teaching strategies that result in the greatest gain in student achievement. One topic we knew would be of value to readers is that of assessment. In the current academic climate, all faculty are exposed to issues related to assessment,

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and many are resistant. Can social psychology speak to this faculty resistance? We asked Dana Dunn to explore this question. Finally, we wanted a capstone chapter to wrap up or summarize the entire volume. We wanted someone with a broad view on the empirical literature and its potential for enhancing teaching and learning. We needed someone who could see the big picture, someone who was familiar with the scholarship of teaching and learning, but also had social psychological expertise. Of course, Randy Smith came to mind. As the editor for Teaching of Psychology for 12 years, he was exactly who we needed. His critical eye and keen insights led to a capstone chapter that is both useful and inspiring. Of course, there were many others who have important work that would have made a great contribution to this volume (for instance, Joshua Aronson’s work on stereotype threat, Carol Dweck’s work on implicit theories, Don Forsythe’s work on groups, to name a few). When one really starts to examine the field of social psychology, almost all of it has implications for teachers. What important territory for social psychologists to explore!

An Emerging Sub-Discipline? We hope this volume spurs what we see as an emerging sub-discipline within social psychology: the social psychology of teaching and learning. By empirically testing classroom practices using methodology from social psychology, the scholarship of teaching and learning becomes more relevant, more useful, and more scholarly. Classrooms and campuses provide real-world settings for testing social psychological theories, thus this emerging sub-discipline promises to enhance the discipline itself while also enhancing the pursuits that sit at the center of institutions of higher education: teaching and learning.

1 How Can Social Psychology Galvanize Teaching and Learning? Regan A. R. Gurung & Kathleen C. Burns

Psychology instructors are lucky people. Social psychologists are particularly lucky. Psychology in general is fun to teach, but social psychology truly takes it up many notches. Social psychology is the study of how we are influenced by other people and the situations we find ourselves in. Is there a more relevant area to help study how teachers and the learning environment can influence learning? Some introductory instructors often move the social psychology section to early in the semester to grab their students’ attention. Others look forward to the social psychology segment at semester’s end to provide a booster shot of adrenaline to tired students. Social psychology is captivating, exciting, often counter-intuitive, and vastly underutilized in the very context where it is first exposed to students— the classroom. Whereas researchers have successfully used social psychology to tackle many contemporary problems (e.g., Abu Ghraib, Zimbardo, 2007; implicit prejudice, Greenwald, McGhee, & Schwartz, 1998), social psychology’s potential contributions to teaching and learning have not been sufficiently mined. There is a sizable literature in the general area of scholarship of teaching and learning (SoTL) but little of this literature explicitly capitalizes on social psychological theories or is firmly grounded in theory at all (Hutchings, 2007). There are also many books that provide teaching tips (e.g., Davis, 2009) or offer advice on how to face challenges of teaching (e.g., Palmer, 2007), but these books only implicitly (if at all) draw on the richness of social psychology as it pertains to the classroom. This volume should begin the process to rectify these wrongs. In this chapter we set the stage for the launching of a new area, the social psychology of teaching and learning. First, we provide a brief history of the general area of SoTL. Then we review the scope of the field of social psychology. Finally, we review existing social psychological forays into SoTL and directly link key areas of social psychology to teaching and learning.

Empirical Research in Teaching and Learning: Contributions from Social Psychology Edited by Debra Mashek & Elizabeth Yost Hammer © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33719-8

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We hope the latter provides a valuable heuristic to optimize teaching and learning as well as to guide future research in this arena.

What is SoTL? Over the past few years there has been much said about what to call research done on one’s teaching. The most commonly used phrase is the Scholarship of Teaching and Learning, more often referred to by its acronym SoTL (though the pronunciation of said acronym varies as one moves across the globe). We define SoTL as intentional, systematic reflections on teaching and learning resulting in peer-reviewed products made public. In addition to SoTL, one also may hear terms such as Scholarly Teaching and Pedagogical Research. A scholarly teacher is someone who intentionally and systematically reflects on and modifies her teaching to enhance student learning (evaluating whether enhancement took place). A scholarly teacher who shares the evidence collected in a peer-reviewed public format, presentation, or publication, is doing what is traditionally referred to as SoTL. If the person does not share their findings it is still scholarly teaching. Pedagogical research (PR) (Gurung & Schwartz, 2009) is a more general term that captures the essence of scholarly work conducted to enhance teaching and advance learning. PR encompasses SoTL and scholarly teaching, and does not imply the results are published or presented (a key part of being labeled SoTL) but by the same token implies a rigorous methodological investigation that goes beyond scholarly teaching. For more on definitional quibbles, the interested reader is urged to peruse Irons and Buskist (2008), Pan (2009), or Smith (2008). In this chapter we opt for the more general term, pedagogical research, in our discussion as it has less of the negative connotations often associated with the term SoTL (e.g., “SoTL is not real research”). The bottom line is that when one is investigating one’s own teaching and one’s own students’ learning, universities and disciplinary departments tend to see the work as falling under the realm of teaching but are beginning to recognize the work as scholarship (or research versus teaching). Some have argued that pedagogical research of this sort should be seen as part as one’s professional responsibility as a teacher (Bernstein & Bass, 2005). Whereas this is not the venue to debate this particular issue further, we hope that the use of the term pedagogical research will make this form of scholarship more likely to be taken as seriously as research on other topics. In addition, we support its consideration as another indicator of excellent teaching. Research is only one part of what we do as educators in academic settings. We all teach. We all step into classrooms (or virtual realities if teaching online) and help quasi-captive audiences of our students to learn a little something of what we know and what we have jurisdiction over. Those who teach hence carry a great responsibility. It is upon

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them we rely to convey the basics about our various disciplines. How do we know if the students are learning? Beyond the simple rubric of exam grades and appreciative nods of understanding lies the challenge we all face as teachers. The challenge is to establish that our teaching is working and our students are learning. Pedagogical research (PR) helps face this challenge. There are other reasons to do PR as well. PR increases our understanding of how and why people learn, gathers evidence of productive teaching and learning, and develops more effective ways to help learners learn better (it is also fun and helps solve vexing mysteries such as “Why did that class go so badly?”).

A Brief History of a Movement Although the term SoTL is a somewhat relative newcomer to the scene, people have been thinking about how to improve teaching and learning for centuries. As Kuh (2004) notes, this “new” line of research, is really a new spin on what researchers in certain fields of study have focused on for decades. It is time for a short excursion into the history of pedagogical research. In a history of the field of educational psychology, Berliner (2006) traces the modern trend of thinking about individual differences, development, the nature of the material being taught, problem solving, and assessment, to the ancient Jewish rite of the Passover. The leader of the Passover service told the story of the Passover each year but differently to each of his sons according to the sons’ own specific aptitudes. This reflects the modern day trend to focus on individual students’ learning styles. Plato and Aristotle are said to discuss such topics as the role of the teacher, the relations between teacher and student, and the means and methods of teaching (Wason, 1960). Writers down the centuries from the Roman Quintilian (1st century), Juan Luis Vives (15th century), Comenius, Herbart (18th century), to the philosopher Joseph Schwab (1973) have also addressed education (Berliner, 2006). Education psychologists have identified a “father of research on teaching,” Joseph Mayer Rice (1912) who conducted empirical classroom-based research and a “grandfather,” William James (1842–1910) who was asked to present Cambridge educators with lectures on the new psychology (Talks to Teachers on Psychology, 1899). Other psychologists have tackled education. G. Stanley Hall, the first president of the American Psychological Association was professor of psychology and pedagogy at Johns Hopkins University. John Dewey, like Hall, was a former classroom teacher who respected the complexity of teaching and also contributed greatly to the methodological study of education (Dewey, 1910). SoTL catapulted into the national higher education consciousness in 1990. It is not that this type of work did not exist before then, but Boyer’s (1990) Scholarship reconsidered catalyzed extensive examination of the work done on teaching and

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learning and flexed the political muscle of organizations such as the Carnegie Foundation for the Advancement of Teaching. The results are staggering. Today enough universities and colleges pay attention to SoTL that books document how the scholarship of teaching and pedagogical research is fostered nationwide (O’Meara & Rice, 2005) and numerous international conferences convene yearly to advance the field. It has been 20 years since Scholarship reconsidered was published and today SoTL is a well-known phrase driving multiple national and international organizations such as the International Alliance for Teaching Scholars (IATS) and the International Society for the Scholarship of Teaching and Learning (ISSOTL). Labels are empowering entities and having a phrase such as SoTL with its conferences and journals has helped further this form of research. Akin to the political force, visibility, and ownership that the politically correct terms such as Asian American and African American gave members of the related ethnic groups, SoTL has provided faculty interested in pedagogical research with a unifying banner to organize around. With the publicizing of the phrase SoTL in response to Boyer and subsequent work of his Carnegie colleagues (e.g., Shulman and Hutchings) among others, this type of research has only been recently recognized in most disciplines as a legitimate area of scholarship, worthy of recognition equal to that of more traditional lines of research and inquiry.

Champions of PR-Working Across Disciplines There are many champions of SoTL. Since 1905, The Carnegie Foundation for the Advancement of Teaching has carried out a wide range of activities and research that has helped to support and advance the work of teachers at all levels. In 1997, the Carnegie Academy for the Scholarship of Teaching and Learning (CASTL) was established when Lee Shulman became president of the Carnegie Foundation. The Lilly Foundation has long been a supporter of teaching enhancement and has been funding faculty scholars nationwide since the 1970s. It also supports an international as well as four national conferences around America. More recently, academic institutions have taken on the mantel of leader by starting up specialized journals. A recent example is the International Journal for the Scholarship of Teaching and Learning (IJSOTL), a peer-reviewed electronic journal published twice a year by the Center for Excellence in Teaching at Georgia Southern University whose first issue hit the electronic airwaves in January 2007. Others who have greatly contributed to pedagogical research are not linked to foundations such as the Carnegie and Lilly foundations. Maryellen Weimer for example, one time associate director of the National Center on Postsecondary Teaching, Learning, and Assessment, and editor of the Teaching Professor newsletter on college teaching, has greatly helped guide and foster pedagogical research

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(see Menges, Weimer, & Associates, 1996; Weimer, 2006). Going beyond the previously discussed traditional sources, it is important also to include a look at how SoTL is done outside America. For example Hounsell and Entwistle spearhead the British Enhancing Teaching and Learning (ETL) Project which seeks to develop subject-specific conceptual frameworks to guide institutional and faculty or departmental development of teaching–learning environments. This group has developed a number of useful tools for pedagogical research and has also mapped out key variables that influence learning (see Entwistle, 2009 for a review).

Champions of PR-Discipline-Specific Activity The disciplines whose names exemplify the topic of interest, Education and Educational Psychology, provide wonderful starting points for a look at how to examine teaching and learning. In addition, many other disciplines and a work of a wide array of scholars (e.g., Calder in history, Hake and Hestenes in physics, McKinney in sociology, Nelson in biology) have conducted research on teaching and learning. PR in various disciplines has been taking place for more time that many may imagine and is more widespread than one may have imagined. In a recent review of the history and diversity of pedagogical research, Weimer (2006) notes that almost all the major disciplines have pedagogical journals. In perhaps one of the most comprehensive listing of publication outlets for pedagogical research, Weimer’s work clearly shows that if one is interested in learning more about how to optimize teaching and learning, there are many places to look (e.g., Journal of College Science Teaching; Active Learning in Higher Education). As a testament to the (mostly unknown) longevity of pedagogical research, the earliest journal articles on teaching and learning were published back in 1924 with the first edition of the Journal of Chemical Education, a publication still in press today. Many of the journals that began a long time ago started as newsletters (e.g., Teaching of Psychology) and conversely, many pedagogical publications are not “published” on paper at all. There are a number of outlets that exist in the electronic World Wide Web only. An example of the extent to which different disciplines are doing PR can also be seen in Exploring signature pedagogies: Approaches to teaching disciplinary habits of mind (Gurung, Chick, & Haynie, 2009). Authors in each chapter in this collection first provide a description of the unique content and characteristic pedagogies in their disciplines. What pedagogies are most often used in the classrooms of the field? They then review and evaluate the pedagogical research related to their discipline, paying special attention to how faculty collect evidence of effective teaching and learning and highlighting what future pedagogical research is

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needed. What does the pedagogical literature of the discipline suggest are the optimal ways to teach material in that field—and verify that learning? Finally, authors assess how the common pedagogies within their disciplines reflect and engage students in the ways of knowing, the habits of mind, and the values used by experts in the field.

Current Controversies in doing PR and SoTL By now, many faculty have at least heard about SoTL. Many faculty are doing some form of pedagogical research, and it is time to move on to new frontiers. Using social psychological theory is one such frontier that we will expand on shortly, but it is prudent to be aware of two major issues raised about SoTL. First, is the issue of where it should be published to count as “SoTL” and how does it count for merit, tenure, and promotion. Many psychologists who conduct pedagogical research write up their work for the premier journal in the teaching of psychology (of the same name). This is commendable given that for many psychologists this may be the first place to look for discipline-specific information, and given the journal has an exceptional reputation with a rejection rate of approximately 85%. But some scholars argue that pedagogical research should be more generally applicable. Weimer (2008) “raises some concerns about positioning scholarship on teaching and learning within the disciplines” and argues that much is “lost when the preference is for pedagogical scholarship owned by the disciplines” (p. 1). Indeed there are many outlets (e.g., the International Journal for the Scholarship of Teaching and Learning) that span disciplines and one could argue (as Weimer does) will do more of a service to the field. The question of where to publish directly links to how PR counts for merit and promotion. Some may argue that publishing in one’s own field (even in a pedagogical journal) will count more towards promotion than publishing in a general SoTL journal (perhaps we do not trust “peers” from other disciplines?). Whereas departments and campuses vary on their attitudes towards how SoTL is viewed towards merit and tenure, the good news is that national surveys show that more and more campuses do look favorably towards PR (Gurung, Kerns, Ansburg, Alexander & Johnson, 2008; Huber & Hutchings, 2005). Furthermore, a number of publications now provide models of ways tenure and promotion committees can, do, and should consider PR (Gurung & Schwartz, 2009; McKinney, 2007). The second issue relates to the need for better theoretical work. Hutchings (2007) identifies this issue seeing “the role of theory in the scholarship of teaching and learning as the elephant in the room” (p. 1). Reflecting on the 2007 annual meeting of the International Society for the Scholarship of Teaching and Learning (ISSOTL) Hutchings notes how many presentations lacked a theoretical

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base. She called for a greater grounding of pedagogical research in theory. Social psychological theories may provide just the answer.

The State of SoTL: A Disciplinary Example Halpern et al. (1998) first attempted to broaden the construct of scholarship to include activities that investigate pedagogy and student learning for the field of psychology. Halpern et al. provided the field of psychology with a “paradigm for the twenty-first century” (p. 1292)—a five-part definition of scholarship that included (a) original research, (b) integration of knowledge, (c) application of knowledge, (d) the scholarship of pedagogy, and (e) the scholarship of teaching in psychology. What is the status of SoTL in psychology now over 10 years since this conceptualization? A task force of the Society for the Teaching of Psychology (Division 2 of the American Psychological Association) conducted a survey to ascertain the degree to which psychology departments and the institutions of higher education that house them have enacted the scholarship of teaching (Gurung et al., 2008). Findings regarding departmental and institutional support for SoTL presented a mixed picture. The field of psychology seems to recognize SoTL better than higher education as a whole (i.e., when compared to the results seen in a survey of higher education by Huber and Hutchings, 2005). For example, 60% of the survey respondents reported having colleagues involved in SoTL, and 78% reported that departmental policies encourage SoTL. That said, doing pedagogical research is clearly not without obstacles for psychologists. Three quarters of survey respondents did not view SoTL as part of their normal scholarship activities, and 75% of the participants indicated they themselves fail to understand what constitutes SoTL. A recent spate of publications (e.g., Gurung et al., 2009; Gurung & Schwartz, 2009) including a special issue on SoTL in the journal Teaching of Psychology (Smith & Buskist, 2008) should help alleviate the issue over definitional confusion and we hope this volume provides the necessary catalyst to further pedagogical research. A number of recent reviews and meta-analyses provide insight into the wide breadth and exact nature of pedagogical research being done in psychology. More importantly, they provide us with key items for our pedagogical research agendas. For example, Peden and Wilson (2009) observe that whereas national guidelines for learning outcomes in psychology suggest how to think like a psychologist, the guidelines do not articulate how to teach it. Peden and Wilson (2009) reviewed back issues of the Teaching of Psychology (2003 to 2007) and made many observations that can provide jumping off points for PR in teaching psychology. In a slightly different vein, Tomcho and Foels (2008) conducted a meta-analysis of 197 studies published in the Teaching of Psychology from 1974 to 2006. They found that, on average, studies evidenced a medium effect size across types of learning

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outcomes. Given the effectiveness of the published teaching activities, the authors suggested that researchers should address (a) the potential confounding role of teacher rapport, immediacy, and alliance in evaluating teaching effectiveness; (b) the ethics of teaching activity development; and (c) the appropriateness of using course grades to assess teaching activity effectiveness. In a content analysis of the same years of content, Tomcho and Foels (2008) identified 15 general teaching strategies in 681 teaching activity articles and coded strategies’ potential impact on student development of scientific inquiry skills. The authors found that authors of articles reviewed had consistently used learner-centered strategies and significantly increased their use of active evaluation strategies. In perhaps the grand-daddy of meta-analyses, Hattie (2009; see also Hattie this volume) analyzed over 800 metaanalyses of studies relating to achievement (a meta-meta-analysis as it were) and lists 131 factors that influence learning. Most recently, Bernstein et al. (2009) and Chew et al. (2010) provide comprehensive pictures of what is known about the processes surrounding teaching and learning and provide general models that can guide future pedagogical research.

Sub-Disciplinary Contributions to PR: Cognitive Psychology Within the field of psychology, cognitive psychology is the next major contributor to studies of teaching and learning (after or perhaps in parallel to the area of educational psychology). Cognitive scientists, who have perhaps the most to offer through well-researched principles of learning and memory, have only recently begun to get involved in classroom research (Metcalfe, Kornell, & Son, 2007). For example, the theoretical characteristics of metacognition have dominated research since the 1960s; however, only recent research has produced research with a focus on educational application. According to Hacker, Dunlosky, and Graesser (1998, p.17) “many researchers [are] convinced of the educational relevance that metacognitive theory has for teachers and students, [and] are shifting their attention from the theoretical to the practical, from the laboratory to the classroom.” A number of lab studies have explicitly demonstrated the benefits of monitoring of one’s thinking (e.g., Dunlosky & Nelson, 1997; Koriat & Bjork, 2005) and cognitive research on metacognition is now beginning to move into real world settings and the classroom. Metacognitive theory can help teachers create classroom environments that foster flexible and creative, strategic learning (Borkowski & Muthukrishna, 1992). This culmination of research suggests students will benefit from teachers who indeed utilize the instruction of metacognitive processes to facilitate learning (see Dunlosky & Lipko, 2007; Hacker et al., 1998; Metcalfe & Greene, 2007 for reviews). Akin to metacogntion, there are many cognitive concepts that can apply to teaching and learning. Some include, temporal spacing (Cepeda, Pashler, Vul,

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Wixted, & Rohrer, 2006), using self-generation rather than reading (Slamecka & Graf, l978), multimodal and contextual variability, spaced practice (Bahrick & Hall, 2005; Pashler, Zarow, & Triplette, 2003), corrective feedback (Butterfield & Metcalfe, 2001), repeated testing (Roediger & Karpicke, 2006), and introducing “desirable difficulties” (Bjork, 1994) such as spacing rather than massing study sessions; interleaving rather than blocking practice on separate topics; varying how to-be-learned material is presented; reducing feedback; and using tests as learning events. Many cognitive concepts have explicit pragmatic implications. For example, because research has shown that retrieval produces robust mnemonic benefits that exceed those of additional study (Kang, McDermott, & Roediger, 2007), testing (i.e., requiring retrieval) may be an especially effective method for improving learning (Karpicke & Roediger, 2008; McDaniel, Anderson, Derbish, & Morrisette, 2007; McDaniel, Roediger, & McDermott, 2007), and have recommended that instructors introduce more quizzing into their courses (Pashler et al., 2007). Work such as this has led to the development and testing of specific strategies for students (e.g., the 3R, read-recite-review strategy, McDaniel, Howard, & Einstein, 2009). As is evident, cognitive psychologists have made explicit attempts to ensure the work from the cognitive laboratory is considered in the classroom. Perhaps one of the best examples of this collection of cognitive theory can be seen in a special issue of the New Directions for Teaching and Learning series dedicated to “Applying the science of learning to university teaching and beyond” (Halpern & Hakel, 2002). Whereas research from the tradition of cognitive psychology identifies core principles that constrain how people learn, the recommendations for teaching that have emerged from this tradition do not always work (Daniel & Poole, 2009). What works in the lab does not always directly succeed in the classroom (Gurung, 2009). Although researchers have started taking cognitive science into the classroom (e.g., McDaniel & Einstein, 2005; Roediger & Karpicke, 2006), the link between making the theory and practice suggested by research applicable to teachers and learners is not complete. Many of the recommendations from controlled lab studies are yet to be translated into practices for the classroom. Furthermore, as much as the areas of educational and cognitive psychology seem to have the corner on SoTL, researchers in these areas primarily treat the classrooms of others as their laboratory (educational psychology) or primarily work in the lab (cognitive psychology). The pedagogical research we advocate puts one’s own classroom, teaching, and learning, under the microscope (see also Smith, 2008). It is also time for social psychology to follow in the footsteps of educational and cognitive psychology. Although social psychology does not at first seem to be as relevant to learning as the areas of education and cognition (relevant almost by definition), it is an area that is perfectly primed to contribute to our understanding of pedagogy. To be fair, this book is not the first time an area of psychology has been

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applied to teaching and learning or the parallels between an area’s focus of study have been mapped onto teaching and learning. Some clinical psychologists have noted the parallel between the client–therapist and the student–teacher. Prieto & Meyers (1999) introduced the concept of the Scientist-Practitioner-Educator in the psychology teaching assistant (TA) training literature to extend a model of counseling (the Boulder model) to teaching, view teaching as professional practice, and identify the need for theory-driven, evidence-based teaching of psychology (Prieto & Meyers, 2009; see also Snyder, 2005). At the APA National Conference on Undergraduate Education in Psychology, Bernstein et al. (2009) offered an expanded view suggesting that the discipline of psychology needs to recognize teaching as a form of professional practice, requiring sufficient preparation to perform competently and ethically, and with training beginning at the graduate level. Similar to the basic call for scholarly teaching, Prieto and Meyers (2009) advanced the notion that training in psychology should adopt the scientisteducator model (similar to the Boulder Model of scientist-practitioner for clinical practice) that involves theory-based teaching, continuous reflection on teaching practices, application of evidence-based instructional strategies, and multifaceted evaluation of teaching and learning outcomes. Using the language of clinical psychology the authors go on to suggest that the essential preparation elements for a scientist-educator qualified to teach at all levels and in all kinds of positions and settings should include a deep knowledge of core psychology, course work in teaching and learning, supervised practicum experience in teaching, and learning how to reflect on and evaluate teaching in a scholarly, theory-driven manner. In direct line with the suggestions of the current volume, Prieto and Meyers (2009) advocate the use of social psychological theory to help explain the effects of TA training (e.g., social cognitive approaches such as self-efficacy). We take this work many steps further to push for the explicit use of social psychological theory and theorizing in pedagogical research. To catalyze this process, we next provide a brief description of the sub-discipline of social psychology and highlight specific ways theories, concepts, and methodologies from it can enhance PR.

What is Social Psychology? Social psychology is the study of how we are influenced by other people. Gordon Allport summarized social psychology as “what we think, feel, say, or do is often affected by what other people are thinking, feeling, saying, or doing” (1985, p. 3). This definition may at first sound like peer pressure, and while social psychology does study conformity, it is much more than that. From persuasion to romantic relationships, social psychologists investigate a myriad of ways that we are impacted by our social environment. Consider the following scenario which makes social psychology’s relevance for teaching clearer: Jenny is sitting in a

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crowded classroom. She raises her hand and answers a difficult question posed by her professor. How did the professor’s expectations of Jenny affect her desire to raise her hand? How difficult did Jenny believe the question was? How did the other students influence the likelihood that Jenny would answer the question correctly? All of these questions are of interest to the social psychologist. Social psychology focuses on how the situations we are in influence our behaviors. People often take for granted the situational forces influencing their behavior and see themselves as independent individuals accurately perceiving their social world. Passersby decide not to help someone on the side of a busy road because they think someone else will. In the classroom, teachers believe that they are treating students fairly, but are often unaware of the role that their prejudices, expectations, and past experiences may have in shaping their students’ behaviors. Students may think that they formed their friendships based on their interests or hobbies, but fail to appreciate the role of proximity (i.e., living next door to each other) in their relationships.

A Brief History of Social Psychology Social psychology is an area of study housed primarily within psychology but with ties to sociology as well. Psychology can be differentiated from sociology in its emphasis on the individual. Even when group behavior is studied, the focus is on the individual in that group. Sociologists study macro-level variables, such as social class, in accounting for our thoughts, feelings, and actions. Social psychology can also be differentiated from other branches of psychology, such as clinical, cognitive, and personality psychology. Clinical psychology focuses on abnormal behavior and psychological disorders whereas the majority of social psychology focuses on normal behavior. Cognitive psychology studies mental processes such as memory, attention, and language. While social psychology is also interested in thought processes, it analyzes these within the context of social interactions. For example, how do people remember information about a person that they just met? Personality psychology is concerned with individual differences such as extroversion and conscientiousness and predicts that people’s behavior is relatively consistent from situation to situation. Social psychologists would expect more flexibility in people’s behaviors depending on the situation that they were in at the time. Often credited as the first social psychology experiment, Triplett (1897) investigated the role of other people in improving performance. Children wound up a fishing reel either alone or with other children present. The children were impacted by their social environment and wound up the fishing reel faster when others were there, showing the first evidence of social facilitation. The first textbooks followed in the early 1900s to further establish social psychology as an area of study.

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The field did not really take off until World War II. World War II inspired such research questions as how to convince families to eat less desirable cuts of meat (attitude change), how soldiers’ morale depended on the other soldiers in their unit (social comparison), and how an atrocity such as the Holocaust could have been carried out (obedience). The war provided the catalyst that social psychology needed to encourage research in a variety of areas beyond its early beginnings in group processes. Today social psychology has grown in scope to study several areas of our everyday lives, including aggression, romantic relationships, helping behavior, prejudice, attraction, impression formation, and many more. Today’s researchers ask questions like what factors predict marital satisfaction or how do our emotions shape our impressions of others? Because of its broad applicability, social psychology is often utilized in other domains such as education, law, and health. It contributes to interdisciplinary questions such as how to manage classroom groups, how eyewitness memory can be biased, and how social support buffers one from stress. Social psychology focuses its lens at three different levels of analysis. Individual processes are at work when considering phenomena such as impression formation, attitudes, and persuasion. Interpersonal processes are at the foreground for romantic relationships, friendships, and helping behaviors. Finally group processes are considered when studying conformity, group decision making, and jury behavior. These levels of analysis demonstrate the breadth of social psychology as well as the different forms of influence that social processes can take. All these levels of analysis, individual, interpersonal, and group, can be used to study teaching and learning. The instructor and student each have intrapersonal factors that influence their teaching and learning. The teacher and student interactions yield emergent properties that contribute to learning (as will be discussed further below). The classroom environment involves group processes (e.g., group work and group presentations) that influence learning.

Methods of Social Psychology The methodological strengths of social psychology provide great tools to stimulate classroom discussion but also lend themselves very well to doing pedagogical research. For example, on the first day of class, both of us present students with several nuggets of common sense wisdom and ask them which are true and false according to the research. This activity usually generates a lot of discussion and interest in the field because many of our common sense preconceptions are wrong. In fact, many of these preconceptions even contradict each other such as birds of a feather flock together vs. opposites attract. Social psychologists use the scientific method to determine under what conditions these various ideas are at work. When

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is it that people will be attracted to similar others? When is it that opposites will be a good match? At first glance, social psychology can seem like common sense: It seems so obvious that people will slack off on group projects. Subjecting such seeming commonsense to scientific examination often yields intriguing results. More importantly, it can yield answers and have significant implications for how we design and teach our classes. Social psychologists are also well-suited to inspire the scholarship of teaching and learning because their typical research participants are college students. Social psychologists use the scientific method to separate fact from fiction. While many of the topics in social psychology have real world implications, what sets it apart from philosophy, common sense, or speculation is the scientific method. Hypotheses must be formed before data are collected. Ideas must be tested with robust research methods. Experimenters randomly assign participants to different experimental groups. Random assignment ensures that participants have an equal chance of receiving an experimental condition and that it is the experimental treatment that is causing the effects observed. Participants do not know the specific hypotheses that are under study and may even be deceived about the study’s true purpose to ensure truthful responding. Results must be replicated in order to establish their reliability. These rigorous procedures ensure the empirical basis of social psychology. There are many different types of research that are carried out to test social psychological ideas. Many of these types of research are directly relevant to research on teaching and learning. Research can be done on an individual, a couple, or even a group. Research can be conducted in a controlled laboratory setting or a natural setting (i.e., field setting). Ideas may spring from observing the real world, past research, or from a larger theory. Some social psychologists focus their attention on basic research questions that focus on theory-building. Basic research increases knowledge about social behavior purely for knowledge’s sake, such as how mood influences judgment. Others focus on applied questions to increase our understanding of and find solutions to real world problems, such as how to eliminate stereotypes and prejudice. Social psychological methods are laudable for their focus on rigor and innovation. As evidence for this, we will describe a few of the many social psychological studies that use interesting methodology and may inspire innovative research designs for classroom pedagogical research. For each, we will summarize the methodology used and provide some fodder for thoughts on pedagogical design. In a classic demonstration of cognitive dissonance, Festinger and Carlsmith (1959) had college participants complete boring tasks for an hour. After they finished these tasks, they were asked if they could introduce the experiment to the next participant because the usual research assistant was gone. Participants were told that they needed to tell the participant the experiment was fun and enjoyable

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because the next participant was in the positive expectation experimental condition. Participants were either paid nothing, $1 or $20 for telling this lie to the next participant. Afterward, participants were asked how much they enjoyed the experiment themselves. Showing evidence of cognitive dissonance, the researchers found that participants enjoyed the experiment more when they were paid $1 to say the experiment was enjoyable than nothing or $20. Because the $1 participants did not have external justification for why they had lied (clearly it was not the money), they changed their attitudes to like the study more to eliminate their feelings of cognitive dissonance. Can we change our students’ attitudes toward learning in the same way? If we got students to acknowledge that others’ textmessaging and web surfing in class is distracting, would it make them less likely to do this themselves? In another ingenious methodological turn, Cohen and his colleagues (Cohen, Nisbett, Bowdle, & Schwarz, 1996) investigated the culture of honor of White Southern men. Students who had grown up in the North or South were asked to fill out a survey and bring it down a long and narrow hallway where there was a confederate filing papers in a cabinet. The hallway was too narrow for the file drawer to be open and for the participant to pass by so the confederate had to close the drawer and move to allow the participant to walk by. The participant had to walk by the confederate again after dropping off his survey so the confederate slammed the file drawer shut, bumped into the participant’s shoulder and called him an “asshole.” Observers were unobtrusively in the hallway and monitored the participants’ emotional reactions to the incident. Southern participants were angrier about the bump than Northern participants. These results reinforced the idea of the culture of honor whereby White Southern men use aggression when their or their family’s honor has been threatened. How do students’ different cultural attitudes towards learning influence their study habits, classroom behaviors, and attendance? Can we design our classes to encompass different cultural attitudes? How do these findings and methodologies influence group dynamics when students from different cultural backgrounds (whether race, ethnicity, or socioeconomic status) are working together? Social psychologists have also turned to indirect measures, such as reaction time to study phenomena where people may not provide truthful responses. In stereotyping and prejudice studies, participants are asked to classify whether words are pleasant or unpleasant by hitting the appropriate key on the computer in the Implicit Association Test (Greenwald et al., 1998). These decisions are included in the same trials as identifying whether a person in a photo is White or Black. The latter decisions can be paired with the same keys as the former decisions. By combining the keystrokes, researchers can measure how participants associate White and Good vs. Black and Bad compared to the alternative pairing White and Bad vs. Black and Good. A faster reaction time represents participants’ stronger implicit attitudes toward the topic. Therefore, if participants are quicker to associate White and Good and Black and Bad, they are showing an implicit

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preference toward White people over Black people. This methodology is especially useful if participants are unwilling or unable to report their true racial feelings. How are students’ implicit attitudes towards the sex and ethnicity of their instructors influencing their learning? Do students’ implicit attitudes about some topics (e.g., evolution) influence their likelihood to pay attention to these topics? What are other implicit processes that could be mapping onto learning? Finally, in a recent methodological advance, social psychologists have turned to biological markers to provide evidence for social phenomena. Self-control can often deplete one’s inner energy reserves, hindering performance on subsequent acts of self-control. For example, dieters follow their diet conscientiously in the morning, but tend to slip in the afternoon and evening hours after a day full of selfregulation. Acts of self-regulation have also been found to be marked by depletion in glucose levels, which can reduce future glucose levels and thereby impair future performance. Gailliot and his colleagues (2007) found that having participants drink lemonade (including glucose) allowed them to counteract the effects of exerting self-control and not show a performance decrement in a subsequent act of self-regulation. This finding has implications for students and teachers in the classroom exerting many forms of self-control and what they eat to replenish themselves. If you teach an afternoon class, should students be provided with tips on how to eat before class? Can snacks before class influence how much attention students will pay and how they will participate in class? Regardless of the research path, there are many practical questions in social psychology. Kurt Lewin, who is often credited as the father of social psychology, is quoted as saying, “No research without action, no action without research.” Even in its early days, social psychology was concerned with the reciprocal nature of research and the real world. Many social psychologists are also interested in studying social issues and problems. In fact, the Society for the Psychological Study of Social Issues was founded by social psychologists, including Lewin, in 1936. Social psychology is rooted in the everyday world and this emphasis can and should be used to make a difference and provide fertile soil for the scholarship of teaching and learning. Its focus on applicable real world questions, its use in educational circles, and rigorous empirical methodology, make it an ideal launching pad for teachers and others interested in the scholarship of teaching and learning. There are many areas of study within social psychology, such as impression formation, aggression, and prejudice, that can inform the classroom experience for both the teacher and student as we shall turn to below.

The Social Psychology of Teaching and Learning Our goal is to kickstart a new focus within social psychology and within the greater field of SoTL. The first call for using social psychology in pedagogical research has already been made—the 2009 Claremont Symposium that drives this volume.

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There has been one significant voice raised before. A special issue of the Journal of Social and Clinical Psychology edited by Snyder (2005) opened “a new turf for the interface of social and clinical psychology (p. 1)” in relation to teaching. Master teachers portrayed a variety of perspectives on college teaching and student learning. Three in particular, explicitly made the case for a greater use of social psychological work (Halpern & Desrochers, 2005; Hammer, 2005; Smith, 2005). We unabashedly stand on the shoulders of these authors to flesh out a call for the use of social psychological theories in pedagogical research. Hammer (2005) provided perhaps the most explicit link between social psychological concepts and teaching. She provides a good starting point for the application of social psychological research in realms such as classroom management (e.g., group dynamics, decision-making, and social norms), explaining student behavior (e.g., attributional biases), and the delivery of information (e.g., persuasion). Her essay focused on research in the area of interpersonal relationship as it applies to student–teacher relationships, especially research on attributional styles, ego depletion, and relationship styles. In the same issue, Halpern and Desrochers (2005) went so far as to call education “applied social psychology” (p. 51). They suggest evoking cognitive psychology research on student-centered learning to help apply social psychological principles. Specially, they discuss the problem of anonymity in classes, and countering it by increasing individual responsibility, accountability, and social comparison. In order to foster a commitment to learning, Halpern and Desrochers (2005) draw on equity and reciprocity theory and using contracts. Similar to Hammer (2005) they also invoke attribution theory and self-handicapping to help understand both faculty and student behavior. Perhaps someone who has written the most about the link between social psychology and the classroom is Smith (2005; 2008; see final chapter this volume). In his piece in the 2005 special issue, he identifies a wide array of social psychological concepts (e.g., fundamental attribution error, social categorization, overjustification effect). He shows for example how a concept such as self-handicapping helps explain why students may not read assignments or study for exams, how the selfserving bias explains why students believe they have performed so much better than they really have, how belief perseverance explains why it is so hard to change the beliefs students bring to class. Of note is that many of the studies cited by Smith (2005) were lab based even though they addressed issues relating to the classroom.

Agenda Setting There are a variety of obvious, and some not so obvious, ways social psychological theories and concepts are applicable to the classroom. Many faculty probably non-consciously utilize social psychological concepts they may have read about.

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Figure 1.1 Possible avenues for the use of social psychological concepts.

As a first step towards providing an agenda for the area of social psychological research on pedagogy, we provide a visual representation of the main factors underlying student learning (see Figure 1.1). Whereas there are many more finetuned details that could be added to this figure, and many different conceptualizations of the same interactions (see Buskist, Carlson, Christopher, Prieto, & Smith, 2008; Chew et al., 2010; Entwistle, 2009; Gurung & Schwartz, 2009), this figure provides a basic lay of the land. Do not let the apparent complexity phase you. First off, it illustrates an important fact we have to come to terms with: Learning IS complex! There is probably no silver bullet or single factor that will make our students learn better. Pedagogical research like any other research can try to limit the factors studied, but especially when you are studying learning in a classroom, as it is happening, we have to acknowledge all the different factors that are possibly playing a part. The main players involved are the teacher, the student, and the interaction between the two. The figure provides some of the factors influencing each of these players. Corresponding to the different factors influencing learning, we list key concepts and theories in social psychology (see Table 1.1). Many of the concepts we list can tie in to the main players in Figure 1.1 and correspond to major areas of teaching and learning. Sometimes the connection can be easily made. For other concepts, a little mental work will show how the social psychological term will influence

Immediacy

Pygmalion Effect

First Impressions Rapport

Impression Formation: the process by which one integrates different information about other people to form an overall judgment about them (Hamilton & Sherman, 1996) Fundamental Attribution Error: explaining other people’s actions in terms of their personality and other internal factors instead of external situational factors (Ross, 1977)

Studying motivation

Desire for change, growth

Lack of student participation

Dealing with exam results

Teaching abilities

Social Cognition: using the methods of cognitive psychology (memory, reaction time, information processing, etc.) to answer questions about our social world (Kunda, 1999) Attributions: how we explain our own and others’ behaviors (Ichheiser, 1943)

Learned Helplessness: a sense of resignation after repeated failed attempts and believing the event is likely to reoccur (Seligman & Maier, 1967) Confirmation Bias: the desire to support one’s own views despite contradicting information (Wason, 1960) Counterfactual Thinking: tendency to imagine alternative scenarios for an event and evaluate the event in relation to these alternatives (Kahneman, 1995) Social Comparison: comparing one’s own outcomes with others and evaluating oneself accordingly (Festinger, 1954) Spotlight Effect: the belief that others are paying more attention to our actions and appearance than they actually are (Gilovich, Medvec, & Savitsky, 2000) False Consensus Effect: overestimating how many other people share your views (Ross, Greene, & House, 1977)

Becoming a critical thinker

Pedagogical Research

Methodological Rigor/Designs

Learning

Teaching

A glossary of major social psychological concepts and potential relationships to elements of teaching and learning

Social psychology concept/contribution

Table 1.1

Philosophy

Attitudes: negative or positive evaluations toward various topics (Eagly & Chaiken, 1993) Motivation: the drive towards completion of a task or goal in which the source can be ourselves or outside factors (Harackiewicz and Elliot, 1993) Persuasion: attempting to change someone’s attitude (Petty & Cacioppo, 1986) Cognitive Dissonance: an unpleasant feeling that results from acting in a way that is not consistent with one’s own attitudes, beliefs or behaviors (Festinger, 1957) Attraction: who we like or love based on factors such as similarity or proximity (Huston & Levinger, 1978) Mere Exposure Effect: the more that people see something or someone, the more they will like it (Zajonc, 1968)

First impressions of teacher and. . .

Workload

Teaching concentration/fatigue

Classroom expectations

Jigsaw classroom

Self-Esteem: a person’s evaluation of themselves and of their worth (Swann, Chang-Schneider, & McClarty, 2007) Self-Concept: how one defines who they are (Snodgrass & Thompson, 1997) Self-Handicapping: acting in a way that will provide an excuse for failure and thereby protect our self-esteem (Arkin & Oleson, 1998) Ego Depletion: showing a decrement in self-regulation performance after initial self-regulation (Baumeister, Muraven, & Tice, 2000)

Self-Serving Bias: seeing oneself in an overly positive way, specifically taking credit for one’s own successes, but blaming the situation for one’s failures (Larson, 1977) Prejudice: negative feelings toward a group of people based on their group membership (Glick & Fiske, 2001) Stereotype Threat: worry about being evaluated according to a negative stereotype, which impairs performance (Steele, 1997) Self-Fulfilling Prophecy: expectations that we have for other people lead us to treat those other people in a way that will actually allow our expectations to be confirmed (Rosenthal & Jacobson, 1968)

(continued)

Formation of study groups Liking of group members, class material

Studying motivation

Malleability of intelligence Depth of learning Cheating

Entitlement Studying/fatigue

Academic performance

(Continued)

Incivility

Aggression: engaging in intentional actions to produce harm (Anderson & Bushman, 2002)

Classroom behavior Group work Group presentations

Learning

Service learning

Teaching performance

Leadership style

Teaching

Helping Behavior: engaging in prosocial actions to benefit others (Penner, Dovidio, Pillavin, & Schroeder, 2005)

Group Processes: the phenomena that account for how two or more interdependent people (a group) are different than an individual (McGrath, Arrow, & Berdahl, 2000) Conformity: when someone changes his or her normal behavior due to the influence of other people (Kiesler & Kiesler, 1969) Obedience: performing an action when given a direct order from someone, especially an authority figure (Milgram, 1963) Deindividuation: when in a group, the tendency to lose one’s sense of self making it easier to act in ways that one would normally not act (Festinger, Pepitone, & Newcomb, 1952) Social Loafing: the tendency for people to not work as hard in a group as they would if they were doing a task on their own (Karau & Williams, 1993) Social Facilitation: the tendency to perform better on familiar and/or easy tasks when in the presence of others (Bond & Titus, 1983) Social Inhibition: the tendency to perform worse on unfamiliar and/or difficult tasks when in the presence of others (Zajonc, Heingartner, & Herman, 1969)

Social psychology concept/contribution

Table 1.1

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teaching and learning. The challenge and exciting part of the road ahead is that many of the connections are understudied. That is where you the reader come in. Instead of spelling out every element of connection we would like to focus on some major elements of connection.

Instructor–Student Interfaces Beyond the methodological contributions of social psychology, both in terms of rigor, design complexity, and inventiveness, there are many aspects of the teaching–learning relationship that can be better understood using social psychology. As seen in Figure 1.1, the factors that contribute to learning can largely be broken down into three main categories: the instructor, the student, and factors arising from the interaction of the two. There are other elements such as the textbook and course design that can be treated in their own right, but are also under the jurisdiction of the instructor. Although social psychology may have once proclaimed a focus exclusively on the situation leaving personal characteristics to the purview of personality psychology, today social psychology succeeds best when taking the interactional approach. Behavior and learning are clearly a function of both the person and the situation. The classroom is perhaps one of the most powerful situations going and social psychology’s focus on the situation is helpful. That said, the most recent reviews suggest that both characteristics of the instructor and the student contribute the lion’s share of the variance in predicting learning (Hattie, 2009). A closer look at many components of teaching and learning (Figure 1.1) in light of the different concepts in social psychology (Table 1.1) provide some exciting avenues for research. Passionate, knowledgeable, organized instructors are well evaluated by their students and the students of such instructors say they learn more (Bain, 2004). Research suggests that there are “ideal” instructor characteristics (Epting, Zinn, Buskist, & Buskist, 2004; Schaeffer, Epting, Zinn, & Buskist, 2003). Epting et al. (2004), for example, found that students’ ideal professor was accessible, personable, flexible, and explicit about course policies. There are many social psychological factors that can help predict how an instructor will perform and how her students will learn, and how she will be evaluated. Right out of the gates, the research on thin slices of behavior suggest that what instructors do in class in the first 5 seconds or so can determine how they are rated at the end of the semester (Ambady & Rosenthal, 1992; Babad, Avni-Babad, & Rosenthal, 2004). Add to this the recent finding that this first impression may even be influenced by an electronic communication sent out before class begins (Wilson, Stadler, Schwartz, & Goff, 2009) really presses for a close look at how first impressions of a class and an instructor can be modified. What does your syllabus say about you? How does your dress that first day set the tone? The very first day of class is ripe with social psychological phenomenon that can influence learning and provides a good illustration of why social psychology

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is important. Paying attention to impression formation in general and the volumes of research on the processes therein can help those first days in the classroom tremendously. There are also many more phenomena that pertain to the first day and that can also influence the rest of the semester. For example, Gurung and Vespia (2007) had 861 undergraduate students complete an online questionnaire rating their instructors. Students were equally spread across class year and were from different majors. The authors used multiple regression analyses and found that likeable, good-looking, well-dressed, and approachable teachers had students who said they learned more, had higher grades, and liked the class better. Results revealed several significant predictors of participants’ class performance and self-reported learning, including student (e.g., GPA), course (e.g., difficulty), and instructor (e.g., likeability) variables. By far the strongest single predictor of self-reported learning, and a significant predictor of self-reported grades, however, was the likeability of the professor. Likeability, in turn, was predicted by instructor attractiveness, approachability, and formality of dress, along with student attendance, participation, and self-reported class difficulty. Attractiveness accounted for the largest proportion of unique variance. Social psychology research consistently finds that people equate beauty with goodness and believe attractive individuals possess numerous positive qualities, but few negative attributes (Weeden & Sabini, 2005). Clearly this finding maps onto classroom life as well. Social psychology can also help solve major recurrent problems in easy ways. Many instructors are pestered with requests for their lecture notes. Others are peeved by students using their laptop computers in class for non-academic purposes. Too many students are using their phones to send and receive text messages. Some students show more extreme cases of classroom incivilities (Boice, 2000). Just saying no, does not always work. Justifying why you are making a request does. In a classic study, Langer (1975) had experimenters try to cut into a line of people waiting to use a copy machine. When the experimenters said, “Excuse me, I have five pages, may I use the copy machine?” only 60% of the people in line agreed. When the experimenter added a justification, “. . .because I have to make some copies” even that statement of obvious fact resulted in 93% compliance. When we ask students not to do something, (e.g., “please do not use any laptops”), adding a simple justification, “Fried (2008) shows that students who use laptops in class do not learn as well/score as highly as those who do not use computers” drives compliance up to 100%. Most importantly, the justification also drives complaining down to zero. That is social psychology in action. And that’s not all. As you can see from Figure 1.1, there are many avenues for intervention, where social psychology can be put to use and its effects on learning assessed. The major concepts of social psychology all pertain to an instructor’s performance: Self-concept, self-esteem, self-efficacy, self-fulfilling prophecy,

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social comparison, confirmation bias, to name but a few can all influence how an instructor performs. One of the most influential books on teaching, Parker Palmer’s The courage to teach (2007) describes how it is critical for a teacher to know themselves well and teach to their strengths. Palmer suggests that techniques, even though they can be learned, are not the hallmarks of a good teacher to the extent that character is. Whereas Palmer is an inspiration to many, his book and many of its ilk do not provide tangible footholds for rigorous study and exploration, for developing a theory of good teaching and learning. The good news is that many of the social psychological terms listed in the preceding sentences underlie what Palmer and many others (e.g., Bain, 2004; Brookfield, 2009) allude to in reference to what makes skillful teachers. Unfortunately, very little pedagogical research has explicitly used these social psychological concepts to drive design and hypothesizing. We hope this book and this chapter changes this status. Many of the same social psychological concepts (e.g., self-handicapping) also influence student behavior. Students often chuckle and nod their heads in disbelief when hearing that participants in Milgram’s (1963) obedience study continued just because the experimenter said “the experiment must continue.” They often fail to notice the extent to which they do things just because the professor asks them to as well. Of course many instructors would like students to do more of what they are told (e.g., read the book) and perhaps there are answers in how to do this in studies of obedience. In like vein, we can use research on phenomena such as the false consensus effect and social norming to decrease inappropriate behaviors. Do students text message in class because they think it is normative to do so? Do students overestimate how many other students text in class and so feel more comfortable doing it? Do students falsely believe that everyone else in class thinks texting is appropriate? These questions are all explained by research in social psychology. Changing social norms has been used to reduce smoking behavior in general (Ahern, Galea, Hubbarde, & Syme, 2009) and in college communities (Gurung, 2010) perhaps it can be used to change classroom behavior including how students study. The false consensus effect, the tendency to attribute own views to others, can also be a powerful explanatory force. In a recent study, Wojcieszak and Price (2009) assessed the association between individual views on several issues such as the death penalty, gun regulation and teaching morality in public schools. They found that there was a correlation between personal and perceived opinion and that those who strongly favored the three policies estimated public support to be higher than do those who are unfavorable or moderate. Studies such as this one provide a good model for how to study our students’ attitudes and design ways to change their behaviors. But again, apart from a few studies such as the one described above, little to no pedagogical research explicitly utilizes social psychological theories and concepts to understand student learning.

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In Conclusion There are a variety of teaching and learning problems that instructors notice on an ongoing basis. There is a correspondingly large volume of social psychological research and theorizing that can be directly applied to better understanding and solving pedagogical problems. Doing pedagogical research and practicing the scholarship of teaching and learning capitalizing on social psychology can greatly advance our understandings of classroom processes. We have identified a few of the many areas above. The list goes on. We know students often feel disconnected in large classrooms. Research on accountability and reducing social loafing can be invoked to remedy the situation and increase engagement. Many students do not work well in groups. Research on group dynamics provide helpful guidelines on how to set up group work. When one takes a close look at what social psychology has studied and the results of years of research from labs a whole new world of applications for the classroom open up. Whereas it is beyond the scope of this chapter to describe and then extensively detail how each social psychological concept can relate to teaching and learning, we hope that the brief glossary in Table 1.1 combined with Figure 1.1 provides the reader with enough to whet their interest for more. We hope they are driven to pick up a social psychology textbook or use an online database to learn more about a concept we have sketched out, and to then map that onto a teaching challenge they have faced or an innovative idea they have had to increase student learning. If we capitalize on the wealth of social psychological research and apply it to the classroom, our teaching and our students’ learning will undoubtedly be the richer.

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Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74, 1464–1480. Gurung, R. A. R. (2009). Applying method to (seeming) madness: Doing SoTL in your class. In S. A. Meyers & J. R. Stowell (Eds.), Essays from e-xcellence in teaching (Vol. 8, pp. 16–18). Gurung, R. A. R. (2010). Health psychology: A cultural approach (2nd edn). San Francisco: Cengage. Gurung, R. A. R. (2010). Using evidence-based pedagogy ethically. In M. A. McCarthy & R. E. Landrum (Eds.), Teaching Ethically: Challenges and Opportunities. Washington, DC: American Psychological Association. Gurung, R. A. R., Chick, N., & Haynie, A. (Eds.) (2009). Exploring signature pedagogies: Approaches to teaching disciplinary habits of mind. Sterling, VA: Stylus. Gurung, R. A. R., Kerns, N., Ansburg, P., Alexander, P., & Johnson, D. (2008). The scholarship of teaching and learning in psychology: A national perspective. Teaching of Psychology, 35, 249–261. Gurung, R. A. R., & Schwartz, B. (2009). Optimizing teaching and learning: Pedagogical research in practice. London, U.K.: Wiley Blackwell Publishing. Gurung, R. A. R., & Vespia, K. M. (2007). Looking good, teaching well? Linking liking, looks, and learning. Teaching of Psychology, 34, 5–10. Hacker, D. J., Dunlosky, J., & Graesser, A. C. (1998). Metacognition in educational theory and practice. Mahwah, NJ: Erlbaum. Halpern, D., & Desrochers, S. (2005). Social psychology in the classroom: Applying what we teach as we teach it. Journal of Social and Clinical Psychology, 24(1), 51–61. doi: 10.1521/ jscp.24.1.51.59165 Halpern, D. F., & Hakel, M. D. (Eds.), (2002). Applying the science of learning to the university and beyond. New directions for teaching and learning. San Francisco: Jossey-Bass. Halpern, D. F., Smothergill, D., Allen, M., Baker, S., Baum, C., Best, D., et al., (1998). Scholarship in psychology: A paradigm for the twenty-first century. American Psychologist, 53, 1292–1297. Hamilton, D. L., & Sherman, S. J. (1996). Perceiving persons and groups. Psychological Review, 103, 336–355. Hammer, E. Y. (2005). From the laboratory to the classroom and back: The science of interpersonal relationships informs teaching. Journal of Social and Clinical Psychology, 24(1), 3–10. doi: 10.1521/jscp.24.1.3.59168. Harackiewicz, J., & Elliot, A. J. (1993). Achievement goals and intrinsic motivation. Journal of Personality and Social Psychology, 65, 904–915. Hattie, J. (2009). Visible learning: A synthesis of over 800 meta—analyses relating to achievement. London, U.K.: Routledge. Huber, M. T., & Hutchings, P. (2005). The advancement of learning: Building the teaching commons. San Francisco: Jossey-Bass.

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2 A Social Look at Student–Instructor Interactions Janie H. Wilson, Karen Z. Naufel, and Amy A. Hackney

The human proclivity to form social connections is a fundamental drive, a drive that is universal, important, and needs to be sated for proper functioning. In this chapter, we will overview concepts related to student–instructor interactions: social connectivity; first impressions; and expectations. We also will examine evidence from the scholarship of teaching and learning that begins to address our hypotheses and point out additional questions for research.

Social Connectivity People’s drive to form positive connections with others is strong. As a result, researchers can create a sense of cohesiveness among people with minimal manipulations (for a review, see Baumeister & Leary, 1995). For example, Billig and Tajfel (1973) found that arbitrarily assigning people to a group was enough to create a favorable attitude toward fellow group members (Billig & Tajfel). Some of the strongest bonds begin simply as a result of two people living close to each other (e.g., Priest & Sawyer, 1967). As Myers (1999) pointed out, “cluster people into groups defined by nothing more than their social security number’s last digit, and they’ll feel a certain kinship with their number mates” (p. 353). However, we should note that people do not form positive relationships with everyone. In fact, many people show signs of anxiety toward unfamiliar others (Duntley & Buss, 2004). Even infants and children exhibit stranger anxiety, especially for male strangers, perhaps because strangers pose the greatest threat Empirical Research in Teaching and Learning: Contributions from Social Psychology Edited by Debra Mashek & Elizabeth Yost Hammer © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33719-8

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to their survival (Duntley & Buss). So people do strive to form positive connections, but only with those who send cues that the interactions will be positive. Such cues often include familiarity, positive verbal exchanges, and nonverbal behaviors.

Cues for Positive Social Interactions Familiarity helps people decipher if a person is approachable and thus worthy of initiating a connection. In general, we prefer familiar people over non-familiar people, even when those familiar to us are presented in an unpleasant context (Saegert, Swap, & Zajonc, 1973). Moreover, familiar faces are perceived as displaying happier facial expressions than unfamiliar faces (Claypool, Hugenberg, Housley, & Mackie, 2007). As Claypool and colleagues explain, seeing familiarity as a positive stimulus may result from the idea that familiarity signals safety. People know what to expect from familiar others; they have no basis to form expectations about unfamiliar people. When we are faced with an unfamiliar person, we seek additional information to evaluate social potential. Within the first few seconds of an interpersonal interaction, we interpret both verbal and nonverbal cues to determine if a person will satisfy the need for a positive connection. Verbal cues are often simple, such as the exchange of a “hello.” Such verbal pleasantries often indicate a desire to maintain a positive connection (Baumeister & Leary, 1995). Other verbal cues may include small talk, such as a comment about the weather. Though the content of small talk may differ among individuals, the underlying goal remains consistent: Small talk offers an opportunity for people to forge positive connections (Coupland, 2003). A specific verbal cue is the use of first names. Morris and Fritz (2000) suggested that learning first names can serve as a way to increase cohesion and cooperation among groups. Additionally, people seem to prefer a “first-name-basis” when establishing social relationships. For instance, 90% of therapy clients preferred to be called by their first name as opposed to a title (Rosenman & Goldney, 1991). Similarly, graduate students found faculty who were called by their first names to be more approachable (McDowell & Westman, 2005). In addition to verbal cues, nonverbal cues, such as facial expressions and physical contact, can communicate a safe bonding environment. For example, smiling is accepted as positive in many cultures and has been associated with prosocial outcomes. In Austria, open-mouthed or spontaneous smiles have been related to cooperative behaviors (Mehu, Grammar, & Dunbar, 2007). In France, smiling female hitchhikers were more likely to receive a ride than non-smiling female hitchhikers (Gu
eguen & Fischer-Lokou, 2004). In Brazil, participants evaluated smiling people more favorably than those who do not smile (Otta, Lira, Delevati, Cesar, & Pires, 1994). In the United States, people gave more tips to wide-smiling cocktail servers than barely-smiling cocktail servers (Tidd &

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Lockard, 1978). Finally, in China, students found smiling people to be more likeable and pleasant than non-smiling people (Lau, 1982). Like smiles, eye contact can help people establish a social relationship, provided that a gaze does not have a threatening undertone. In general, eye contact is associated with positive attributes such as liking and competency (for a review, see Kleinke, 1986). Further, eye contact is greater between the audience and speaker when the audience likes the speaker (Mehrabian, 1968). Eye contact has also been tied to increased self-disclosure (as would be found in more established social relationships). When audience members shared a gaze with a speaker, female speakers revealed more personal information (Ellsworth & Ross, 1975). Even touch can indicate a positive connection, provided that the touch is appropriate (for a review, see Hertenstein, Verkamp, Kerestes, & Holmes, 2006). Simple exchanges of touch can convey trust, making a person seem more trustworthy than if no touch had been exchanged (Burgoon, 1991). As people are touched, they are more likely to engage in behaviors that suggest a positive connection. For example, people were more likely to engage in prosocial behaviors, such as picking up dropped diskettes (Gu
eguen & Fischer-Lokou, 2003) or leaving greater tips to a restaurant server (Crusco & Wetzel, 1984), if the recipient of these benevolent acts had first made physical contact. Together with other forms of nonverbal behaviors, it seems that touch facilitates social bonding with others.

Effects of Social Connectivity Scientists have argued that the ability to bond with others facilitates survival (e.g., Baumeister & Leary, 1995; Ohtsuki, Hauert, Lieberman, & Nowak, 2006). In order for humans to survive, people had to work together to obtain food, build suitable shelter, and raise their young (Baumeister & Leary). Indeed, empirical evidence reveals a multitude of benefits for those with positive connections. Benefits can be categorized as physical, emotional, cognitive, and behavioral. Physical benefits for those who are socially connected include better health than those who lack significant positive connections (Baumeister & Leary). Additionally, having positive social interactions is related to enhanced immune functioning (Cohen, Doyle, Turner, Alper, & Skoner, 2003). Positive social connections are related to positive emotional states. For example, married people tend to be happier than divorced, widowed, or single people (Baumeister & Leary, 1995), and coworkers who like their fellow coworkers tend to be happier than coworkers who do not (Loscocco & Spitze, 1990). In contrast, if a person fails to establish positive connections, a whirlwind of negative emotions typically emerge. Excluded individuals often feel anger or sadness (Williams, 2007). Exclusion causes hurt feelings, even when being excluded has monetary rewards. In one study, participants were financially rewarded if they could get others to ostracize them (van Beest & Williams, 2006). Though participants received

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financial gain for their failure to connect with others, these participants still felt more negative emotions than those who had connected with others. Positive connections, or the lack thereof, also affect people’s thought processes. In a series of three experiments, Baumeister and colleagues revealed how social exclusion affected learning and cognition (Baumeister, Twenge, & Nuss, 2002). In each experiment, the researcher told participants that they would either live a lonely life (e.g., be socially rejected), experience misfortune, or form meaningful relationships. Socially rejected persons performed worse on intelligence tests (Experiment 1), recalled less information from a previously read passage (Experiment 2), and exhibited inferior logical and reasoning skills (as defined by the number correct on the analytical section of the Graduate Record Examination [GRE]) than non-rejected participants. Together, these experiments suggest that social exclusion can hamper the ability to think about complex information. However, positive connections can increase cognitive functioning. As Ybarra et al. (2008, Study 2) showed, a minimal ten minutes of social interaction were enough to increase working memory and processing speed on an intellectual task. Beyond physical, emotional, and cognitive outcomes of socialization, positive social connections correlate with behaviors such as productivity. Receiving social support from immediate supervisors is related to increased productivity (BaruchFeldman, Brondolo, Ben-Dayan, & Schwartz, 2002). Similarly, medical students self-reported greater productivity the more they interacted with their peer groups (Das Carlo, Swadi, & Mpofu, 2003). One potential explanation for increased productivity in a healthy social environment is the ability to self-regulate, or control the self. Those who enjoy social connections with others are able to dedicate more time and energy to nonsocial tasks, hence becoming more productive. Conversely, those who lack social connections will be less likely to control themselves because a wealth of time and energy will be dedicated to seeking out social interaction. That is, self-control capacities are somewhat limited, meaning that regulating one aspect of the self depletes the ability to regulate other aspects of the self (Baumeister, Bratslavsky, Muraven, & Tice, 1998). Indeed, people who are ignored focus on how to reestablish positive relationships; people who are rejected focus on how they could have changed the situation for the better (Molden, Lucas, Gardner, Dean, & Knowles, 2009). These people thus may be devoting their limited energy to establishing social connections rather than completing other important tasks. Similarly, ignored women often put more effort into their group’s projects to redeem their sense of belongingness (Williams & Sommer, 1997). Though their disconnection with others may drive them to establish positive connections, their effort in satisfying this drive may deplete their self-control resources. More simply put, feeling excluded drains the ability to control the self, motivation that could be used for other activities, such as learning.

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In support of this notion, research has shown that people who repeatedly try and fail to form positive connections show deficits in self-regulation. Rejected persons were easily tempted by enjoyable activities, procrastinated, or engaged in risky behavior (Twenge, Catanese, & Baumeister, 2002). Additionally, socially rejected people often failed to accomplish goals effectively. They were less likely to think about the future consequences of their actions or put less effort into a writing task (Twenge, Catanese, & Baumeister, 2003). Thus, the failure to form positive connections hampers the pursuit of a wide variety of goal-oriented behaviors. As we can see, empirical evidence illustrates that positive connections benefit people by enhancing physical health, positive emotions, cognitive functioning, and behavioral outcomes such as goal-oriented behavior and productivity. It takes minimal effort to establish a positive connection with someone. Beyond familiarity, mere greetings, small talk, and the use of the first name all indicate that a person is approachable and worth the effort of initiating a positive relationship. Even nonverbal behaviors, such as smiling, eye contact, and appropriate touch, can communicate that a positive interaction is likely, as long as the nonverbal behaviors are appropriate and genuine.

Social Connectivity Informs Teaching and Learning Positive outcomes of social connectivity extend to the student–teacher relationship, illustrating what has been called “the relational teaching approach” (DeVito, 1986). The concept of relational teaching recognizes the social context of the classroom and offers a new lens through which we can evaluate our teaching. Both veteran and first-year teachers reported that positive relationships with students are crucial to job satisfaction (Graham, West, & Schaller, 1992, and Fink, 1984, respectively). In fact, Graham and colleagues suggested that failure to engage in positive interactions with students may lead to career burnout (see also Teven, 2007). Machell (1989) offered the term “professorial melancholia” to describe the primary facet of teaching burnout. The first stage of professorial melancholia is characterized by resenting students, with intensity increasing in stage two. The third and final stage includes verbal and grade abuse of students and the belief that students are enemies. Therefore, teachers need to recognize their own need for positive interactions with their students in order to enjoy the day-today responsibilities of teaching. Teachers can create a positive social connection with their students through familiarity, which can be fostered by faculty–student contact such as meetings outside of class or electronic communications. Anecdotal evidence suggests that students want teachers to talk with them before or after class (Merwin, 2002). Similarly, several correlational studies revealed that students who had frequent contact with a professor not only trusted that professor more, but they also selfreported greater motivation to succeed in the course (Fusani, 1994; Jaasma &

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Koper, 1999). Moreover, Jaasma and Koper revealed that informal contact, rather than classroom-related discussion, was related to trust in the instructor. To this end, some schools encourage teachers to socialize with students outside of class, such as visiting faculty homes. However, even physically distanced electronic communication can enhance the student–teacher relationship. Sheer and Fung (2007) reported that the frequency of email exchange, prompt response from the professors, and friendly tone of the email positively related to students’ trust of the instructor and students’ evaluation of the instructor. Thus, teachers have numerous options to capitalize on the impact of familiarity with students, and we would predict that increased familiarity, at least to a reasonable degree, would be associated with positive student outcomes. In fact, positive student–teacher relationships characterized by friendliness and caring define rapport (Altman, 1990), and rapport predicts student outcomes. Benson, Cohen, and Buskist (2005) asked students to create a list of teacher qualities that build rapport; in order, qualities were “encouraging, open-mindedness, creative, interesting, accessible, happy, having a `good´ personality, promoting class discussion, approachability, concern for students, and fairness” (p. 238). Lowman (1994) referred to “interpersonal rapport” as a crucial part of effective teaching, and Murray (1997) argued that student–teacher rapport is associated with student learning. Similarly, students who believed they had experienced rapport with their professor reported greater enjoyment of the material covered in the course and of the instructor as well as rated themselves more likely to attend class, study, contact their professor, and engage in other academically helpful behaviors (Benson et al., 2005). Finally, students who reported rapport with their instructor also reported greater attentiveness in class (Buskist & Saville, 2004). Therefore, numerous positive student outcomes have been associated with rapport in the classroom. One aspect of rapport is immediacy, defined by psychological availability. Conveniently, immediacy in teaching has been studied for decades and includes both verbal and nonverbal teacher behaviors. Verbal behaviors include calling students by their first names, calling on students during class to request their input, and using humor in the classroom (Gorham, 1988). Nonverbal behaviors include smiling, eye contact, and maintaining a relaxed posture while teaching (Andersen, 1979). Because teacher verbal and nonverbal behaviors can build positive social connections with students, we would expect immediacy behaviors to be associated with positive student outcomes such as improved student attitudes, motivation, and enhanced learning. Based on the fact that people can manipulate (fake) their verbal interactions better than their nonverbal responses (DePaulo, 1992), we would expect nonverbal immediacy to be the best predictor of student outcomes. The available immediacy literature is replete with examples of positive student outcomes based on teachers communicating psychological availability. Nonverbal

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immediacy has served as an excellent predictor of student outcomes, including motivation to do well in the course (Christensen & Menzel, 1998; Christophel, 1990; Christophel & Gorham, 1995; Frymier, 1993), perceptions of learning (Christensen & Menzel, 1998; Witt, Wheeless, & Allen, 2004), and attitude toward both the course and the instructor (Andersen, 1979; Andersen, Norton, & Nussbaum, 1981; Christensen & Menzel, 1998). Instructor verbal immediacy also has been associated with student motivation (Frymier, 1993), perceptions of learning (Menzel & Carrell, 1999; Witt et al., 2004), and both attitude toward the course and the instructor (Moore, Masterson, Christophel, & Shea, 1996). It is interesting to note that students are accurate when judging their teachers’ attitudes toward them, with high correlations between students’ rating of their instructor’s attitude toward them and the instructor’s actual attitude toward students (r ¼ 46; Wilson, 2008). Unfortunately, the bulk of immediacy research is correlational. The available literature lacks experimental manipulation of immediacy to examine cause and effect with respect to student outcomes. In addition, the possibility of too much teacher immediacy exists (e.g., Comstock, Rowell, & Bowers, 1995), with overly immediate teachers making students feel uncomfortable rather than welcome. Finally, teachers and students would benefit from knowledge of precisely which immediacy behaviors are most beneficial to students. For example, is smiling the crucial behavior? Would calling students by name be sufficient to create rapport? Finally, would appropriate touch affect student perceptions? In a recent examination of the latter question, students were touched on the hand during a welcoming handshake for a randomly-selected half of the students (Wilson, Stadler, Schwartz, & Goff, 2009). A handshake was chosen as an appropriate, professional way to touch students and welcome them on the first day. In the primarily female sample, students who were touched by female professors had significantly more positive attitudes toward the teacher than those who did not receive the handshake. However, students of male teachers reported poorer attitudes and motivation following the handshake compared with those who were not touched by male teachers. Based on these results, we can see that touching students may be beneficial (at least a handshake with female students) but only when the touch is provided by a female professor. Clearly, many questions about touch in the classroom remain unanswered, and both immediacy and rapport remain fruitful areas of research in teaching and learning.

First Impressions As we seek out and engage in social interactions, we must recognize the power of first impressions. For example, research has shown that people make impressions of others regarding attractiveness, aggressiveness, competency, likeability, and trustworthiness following a mere 100 ms exposure to a face, and these impressions

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do not differ from those made without time constraints (Willis & Todorov, 2006). Further, automatic impressions can be remarkably accurate. For instance, people can detect male sexual orientation at better than chance levels after only 50 ms of exposure to a face (Rule & Ambady, 2008). Unfortunately, these impressions also can be quite inaccurate, falling prey to stereotypes and prejudice (e.g., Banaji, Hardin, & Rothman, 1993; Devine, 1989). Regardless of accuracy, it is part of human nature to form rapid opinions of others due to our need to understand our environment (Fiske, 2004). In addition to our tendency to form quick impressions of others, our first impressions are resistant to change (Fiske, Lin, & Neuberg, 1999), especially if the view of another is negative (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001). To demonstrate the pervasiveness of first impressions, Rosenhan (1973) conducted a study in which eight experimenters, all mentally healthy, were admitted into mental institutions after complaining that they had auditory hallucinations. After they were admitted with the diagnosis of schizophrenia, they acted completely normal and behaved truthfully. When they were finally released, it was with the diagnosis of schizophrenia in remission. This study is a vivid example that after a perception of a person has been formed, that person is viewed through the subjective lens of the perception (in this case, abnormal), and it is very difficult to perceive new, inconsistent information.

Forming First Impressions That Last First impressions play a powerful role in our thoughts, feelings, and behaviors toward others. More specifically, first impressions impact the information we notice and attend to about another, the interpretation of that information, the memory of the information, and how we subsequently act toward the other person (Fiske, 2004). First impressions are often based on physical attractiveness, with even babies as young as 6 months showing the ability to categorize attractive faces and unattractive faces as belonging to different groups (Ramsey, Langlois, Hoss, Rubenstein, & Griffin, 2004). People deemed physically attractive are viewed more positively than unattractive people. This ubiquitous preference for attractive others is often referred to as the “what is beautiful is good” effect (Dion, Berscheid, & Walster, 1972). Miller (1970) showed that participants expected attractive people to have more positive traits than unattractive people, including the traits of happy, amiable, active, pleasure-seeking, curious, complex, perceptive, humorous, outspoken, and flexible. Further, judges give smaller fines to attractive defendants compared to unattractive defendants (Downs & Lyons, 1991). Recent work suggests that the preference for attractive individuals over unattractive individuals is driven more by the belief that what is ugly is bad rather than what is beautiful is good (Griffin & Langlois, 2006). Ultimately, the perceiver tends to favor the beautiful and disparage the ugly.

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First Impressions Inform Teaching and Learning Whether we engage in verbal or nonverbal interactions with our students, they will quickly form impressions of us, and these first impressions are enduring. Thus, we might examine empirically the potential effect of the first day of class on student perceptions, motivation, and performance (such as grades). We would expect early impressions to form the basis for student responses to us such that students see what they expect to see from us the remainder of the term. Furthermore, we would expect the first day of class, indeed any early interaction, to impact student attitudes, motivation, and learning. None of us would argue that the first day of class is unimportant; however, we might disagree on the best way to create our first impression to students. McKeachie (2002) suggested using the first day to get acquainted and establish course goals as well as cover the syllabus, the textbook, and even some lecture material. McKeachie insisted that the first day must be used in its entirety to communicate the importance of class time. Perlman and McCann (1999) reported that on the first day, students want to know about the course content and expectations, grading standards, and the amount of work required. Similarly, Henslee, Burgess, and Buskist (2006) found that students were most interested in learning the content of the syllabus and obtaining a general overview of the course. Based on these results, some student preferences appear to be in line with the advice of teaching gurus. However, students also described their pet peeves, which included beginning course material, using the full time allotted for the class, and assigning homework on the first day (Perlman & McCann). Students also reported their dislike for an uncaring, intimidating instructor. Similarly, Henslee and colleagues found that students did not want to begin course material on the first day. Clearly, first-day student preferences and instructor practices reveal a disparity. In an experimental evaluation of the first day of class, Wilson and Wilson (2007) randomly assigned students to have a first day that (a) covered the syllabus, began class material, and assigned homework, all by a straightforward professor, or (b) covered the syllabus and dismissed class, as presented by a friendlier professor (e.g., smiling). Students in the latter condition had higher motivation across the term and higher final grades at the end of the term than students in the former condition. Because this study was the first experimental manipulation of the first day of class, the two groups differed in several ways, including the level of rapport inspired by the professor in the two conditions. The possibility exists that the friendly nature of the professor was sufficient to increase motivation and grades, however, numerous potential confounds exist and leave room for future research. Subsequent research on the first day of class could evaluate exactly which aspects of the first day are important for positive student outcomes (e.g., friendliness of professor, not lecturing on the first day, or not giving homework on the first day).

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Regardless of which component of the first day might explain results, students formed lasting and powerful first impressions of the instructor, and these impressions altered long-term motivation and grades. In fact, first impressions are so powerful that we might help students form first impressions even before the first day of class. Legg and Wilson (2009) sent a welcoming email to a randomly-selected group of students; the email was sent one week prior to the first day of class. Throughout the term, students (particularly females) who had received the email had more positive attitudes than those who had not received the email. Further, retention was significantly higher in the email condition. As an added benefit, electronic contact can remove expectations based on beliefs that what is beautiful is good such that teachers are not judged based on their appearance. With no visual information, students would be forced to form their enduring first impressions with less information. Similarly, electronic communication would reduce potential negative stereotypes based on culture or ethnicity. Future research in this area of teaching and learning could examine out-of-class communication to see if the welcoming email solicited more student–teacher interactions. Because of the positive impression formed after receiving the email, students may have sought out the professor more often for help and support across the semester. Thus, the email paired with increased student–instructor contact could have produced the long-term effects. In addition, prior results may be specific to the female instructor in the study, requiring additional research on gender effects produced by male instructors as well. As technology continues to offer more opportunities to communicate, professors can choose from many ways to contact students, with early contact having the potential advantage of improving retention. The most obvious contact is email, with benefits for female students but questionable outcomes for males, at least when contact is by a female professor (Legg & Wilson, 2009). Perhaps a welcoming email with a calmer tone (e.g., no exclamation points) than used in this study would be received better by male students. Alternatives include any other computer contact (instant messaging or communication through social networking sites such as Facebook, for examples), which allows a large number of students to be welcomed to class even before they set foot in the door. Such forms of communication are a relatively effortless way to enhance student attitudes toward the instructor and the course and improve retention.

Expectations Regardless of the basis for first impressions, perceptions of others are enduring. One of the reasons for pervasive first impressions is the confirmation bias, in which people seek out information that confirms their initial hypothesis or expectation

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and ignore or distort information that is contrary to initial expectations. In a classic study by Snyder and Swann (1978), interviewers who were instructed to determine whether the interviewees were extraverted found them to be more outgoing than interviewers who were instructed to determine whether the interviewees were introverted. These differential perceptions occurred because the interviewers asked different hypothesis-confirming questions of the interviewees. For example, extraverted-instructed participants were more likely to ask questions such as “What would you do if you wanted to liven things up at a party?” and “In what situations are you most talkative?” but introverted-instructed participants were more likely to ask questions such as “In what situations do you wish you could be more outgoing?” and “What things do you dislike about loud parties?” Another reason for pervasive first impressions is that after an impression is formed, it is difficult to shake. Even if people are told outright that their initial impressions are false, the first impression still tends to stick, an effect known as belief perseverance (Anderson, 1995).

Creating Our Own Reality with Expectations The effects of expectations go beyond influencing the information processing and behavior of the perceiver. Research has also demonstrated that perceiver expectations can bring about the expected behavior in the target (Rosenthal, 1994). Essentially, people treat others in line with their expectations and create a selffulfilling prophecy (e.g., Snyder, Tanke, & Berscheid, 1977). Numerous articles in support of the self-fulfilling prophesy exist in the available literature. For example, in a classic study, White participants who interviewed a Black job applicant sat further away from him, gave him less time to talk, and made more speech errors than participants who interviewed a White job applicant. A new group of White participants were then given either the “Black treatment” or the “White treatment” by trained White researchers. Results showed that those applicants given the “Black treatment” had poorer interview skills (Word, Zanna, & Cooper, 1974). Thus even subtle cues of a perceiver can elicit expected behavior in others. Similarly, when job trainers expected obese female trainees to perform poorly during a training session, they did, not because they were less skilled, but because the trainers treated them more negatively than their average-weight peers (Shapiro, King, & Qui~ nones, 2007). Perceivers with positive expectations exude warmth toward the target and spend more time and effort with the target working on the task at hand. They also engage in several positive behaviors, including smiling, eye contact, close proximity, and touching. Perceivers with negative expectations have a cold demeanor and spend less time and effort with the target to accomplish a task. By the same token, they also engage in several avoidant behaviors, including lack of eye contact and increased distance.

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Expectations Inform Teaching and Learning In the classroom, teachers’ perceptions of students can bring about the expected behaviors (self-fulfilling prophesy). For example, Rosenthal and Jacobson (1968) told teachers that some of their students were “bloomers,” (i.e., students who would grow intellectually over the course of the school year). Consequently, those students actually performed better one year later on an IQ test than students who were not labeled as bloomers. Teachers treated bloomers more kindly, gave them better feedback on assignments, taught them more material, and gave them longer to respond to questions. Similarly, if teachers expected physically attractive students to perform better in school than less attractive students, they did, not because they were more intelligent, but because the teachers gave them more warmth and attention than their less-attractive peers (Feingold, 1992;Harris & Rosenthal, 1985; Ritts, Patterson, & Tubbs, 1992). Even if people are motivated to treat others equally, perceivers’ body language and other nonverbal cues unintentionally change according to expectations. Chaiken, Sigler, and Derlega (1974) found that participant teachers who expected a student to be very bright engaged the student more by leaning forward, looking the student in the eye, nodding their heads, and smiling more compared to teachers who expected a student to be slow or who had no prior expectations. By the same token, Babad, Bernieri, and Rosenthal (1991) asked teachers to identify students for whom they had low or high expectations, followed by a brief lesson that was recorded. The teachers were also recorded talking about these students. Independent judges rated thin slices of the tapes of teachers (10 seconds) for characteristics such as warmth, dogmatism, condescension, hostility, and dominance. When teachers talked about low-expectancy students, the teachers were rated as less warm and more dogmatic. When they taught the lesson to lowexpectancy students, the teachers were rated as having more negative body language (but not negative facial expressions) as compared to when they taught high-expectancy students. Thus the teachers were effective in suppressing negative expectations from their faces, but other negative body language was more difficult to control. Further, students can detect the meaning of a teacher’s body language. In one study (Babad, 2005), high-school students watched thin slices of unknown teachers’ nonverbal behavior as the teachers gave lectures to their classes. Based on this brief exposure to nonverbal behavior, the students were able to successfully predict (r ¼ .40) which teachers treated their low-achievement students differently (e.g., shorter duration of eye contact with low-achieving students) than their highachievement students (e.g., longer duration of eye contact with high-achieving students) and which teachers treated their low-and high-achievement students equally. Interestingly, adult observers were unable to make this prediction, indicating that students are particularly in tune with the relationship between

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teachers’ nonverbal behaviors and their thoughts and feelings toward students. As we can see, teacher nonverbal behaviors may be difficult to control, and students seem to be accurate when assessing the meaning of nonverbal cues. Further, differential teacher behaviors have the potential to create a self-fulfilling prophecy when it comes to student outcomes.

Conclusions In summary, it is natural for people to try to understand the personalities of others and to predict future behaviors. We make such judgments quickly and often. We need to be careful of the impressions we form, however, because first impressions are usually lasting impressions. Likewise, we need to be careful about the messages we “put out there” to others, because a negative impression of us may be hard to shake. Furthermore, first impressions may actually lead to the expected behavior through the creation of self-fulfilling prophecies. Even people who are motivated to treat others equally may unintentionally reveal negative expectations and create negative behavior through nonverbal communication. Thus, we can see that social relationships are fraught with misunderstandings and bias. As teachers, we understand that errors in social relationships with students may lead to negative student outcomes. In this chapter, we discussed several potential applications of social connectivity to teaching. We recognize that social relationships in the classroom have two sides: The teacher and the students. Certainly, teachers have perceptions of students that may reflect errors in judgment. That is, we as teachers should consider the importance of positive social relationships with our students, and we can fall prey to first impressions, the “what is beautiful is good” effect, confirmation bias, and the self-fulfilling prophesy. Beyond establishing the importance of relationships, this chapter focused on students’ first impressions of their teacher, students responding to the physical attributes of their teachers, and students influenced by confirmation bias and the selffulfilling prophesy, all which can foster or hamper student–teacher rapport. Our chapter focused on how we as teachers might manipulate the social environment such that students benefit by improved attitudes, enhanced motivation, and increased learning. Based on the examples of research on teaching and learning offered in this chapter, we can begin to see the potential for social connectivity to impact our classroom experience. A plethora of questions remains to be answered, with each answer spawning new questions, new hypotheses. We are experiencing an exciting development in teaching and learning: true empirical research to finally test our ideas and improve life for ourselves and for our students.

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Chaiken, A. L., Sigler, E., & Derlega, V. J. (1974). Nonverbal mediators of teacher expectancy effects. Journal of Personality and Social Psychology, 30, 144–149. Christensen, L. J., & Menzel, K. E. (1998). The linear relationship between student reports of teacher immediacy behaviors and perceptions of state motivation, and of cognitive, affective, and behavioral learning. Communication Education, 47, 82–90. Christophel, D. M. (1990). The relationships among teacher immediacy behaviors, student motivation, and learning. Communication Education, 39, 323–340. Christophel, D. M., & Gorham, J. (1995). A test-retest analysis of student motivation, teacher immediacy, and perceived sources of motivation and demotivation in college classes. Communication Education, 44, 292–306. Claypool, H. M., Hugenberg, K., Housley, M. K., & Mackie, D. M. (2007). Familiar eyes are smiling: On the role of familiarity in perception of facial affect. European Journal of Social Psychology, 37, 856–866. Cohen, S., Doyle, W. J., Turner, R., Alper, C. M., & Skoner, D. P. (2003). Sociability and susceptibility to the common cold. Psychological Science, 14, 389–395. Comstock, J., Rowell, E., & Bowers, J. W. (1995). Food for thought: Teacher nonverbal immediacy, student learning, and curvilinearity. Communication Education, 44, 251–267. Coupland, J. (2003). Small talk: Social functions. Research on Language and Social Interaction, 36, 1–6. Crusco, A. H., & Wetzel, C. G. (1984). The Midas touch: The effects of interpersonal touch on restaurant tipping. Personality & Social Psychology Bulletin, 10, 512–517. Das Carlo, M. D., Swadi, H., & Mpofu, D. (2003). Medical student perceptions of factors affecting productivity of problem-based learning tutorial groups: Does culture influence the outcome? Teaching and Learning in Medicine, 15, 59–64. DePaulo, B. M. (1992). Nonverbal behavior and self-presentation. Psychological Bulletin, 11, 203–243. Devine, P.G. (1989). Stereotypes and prejudice. Their automatic and controlled components. Journal of Personality and Social Psychology, 56, 5–18. DeVito, J. A. (1986). Teaching as relational development. New Directions for Teaching and Learning, 26, 51–59. Dion, K., Berscheid, E., & Walster, E. (1972). What is beautiful is good. Journal of Personality and Social Psychology, 24, 285–290. Downs, A. C., & Lyons, P. M. (1991). Natural observations of the links between attractiveness and initial legal judgments. Personality and Social Psychology Bulletin, 17(5), 541–547. Duntley, J. D., & Buss, D. M. (2004). The evolutionary psychology of good and evil. In A. Miller, (Ed.), The social psychology of good and evil (pp. 102–126). New York: Guilford. Ellsworth, P., & Ross, L. (1975). Intimacy in response to direct gaze. Journal of Experimental Social Psychology, 11, 592–613. Feingold, A. (1992). Good-looking people are not what we think. Psychological Bulletin, 111, 304–341.

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Fink, L. D. (1984). First year on the faculty: Being there. Journal of Geography in Higher Education, 8, 11–25. Fiske, S. T. (2004). Social beings. A core motives approach to social psychology. Hoboken, NJ: Wiley. Fiske, S. T., Lin, M., & Neuberg, S. (1999). The continuum model: Ten years later. In S. Chaiken & Y. Trope (Eds.), Dual-process models in social psychology (pp. 231–254). New York, NY: Guilford Press. Frymier, A. B. (1993). The relationships among communication apprehension, immediacy and motivation to study. Communication Reports, 6, 8–17. Fusani, D. S. (1994). `Extra-class´ communication: Frequency, immediacy, self-disclosure, and satisfaction in student–faculty interaction outside the classroom. Journal of Applied Communication Research, 22, 232–255. Gorham, J. (1988). The relationship between verbal teacher immediacy behaviors and student learning. Communication Education, 37, 40–53. Graham, E. E., West, R., & Schaller, K. A. (1992). The association between the relational teaching approach and teacher job satisfaction. Communication Reports, 5, 11–22. Griffin, A. M., & Langlois, J. H. (2006). Stereotype directionality and attractiveness stereotyping: Is beauty good or is ugly bad? Social Cognition, 24, 187–206. Gu
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3 Self-Construal, Culture and Diversity in Higher Education Shelva Paulse Hurley and Eric Alexander Hurley

When those who have the power to name and to socially construct reality choose not to see you or hear you, whether you are dark-skinned, old, disabled, female, or speak with a different accent or dialect than theirs, when someone with the authority of a teacher, say, describes the world and you are not in it, there is a moment of psychic disequilibrium, as if you looked into a mirror and saw nothing. (Adrienne Rich)1

Institutions of higher education the world over are charged with training the future professionals, scholars and leaders of nations. Although the number and range of institutions is greater than ever, there is a surprising uniformity in the basic orientation, values and priorities of higher education institutions. In fact the basic formula for how higher education is “done” has remained unchanged for at least a century (Altbach, Berdahl, & Gumport, 2005). By contrast the corresponding growth in the number of students seeking higher education has brought significant changes in the demographic “look” of students at many of these same institutions (Solmon, Solmon, & Schiff, 2002; Kurlaender & Flores, 2005). Shifts in the gender, racial, ethnic and socioeconomic profiles, and of interest here, cultural backgrounds of students have lead to increasing diversity in the basic orientation, values, and priorities of higher education students. There is reason to think that this shift has lead to incongruities between institutional assumptions about, and students’ culturally derived orientations concerning, the appropriate relationship between “individuals,” “others,” and “institutions.” Such discord can complicate transactions between higher education institutions and the students they mean to serve, and may be at the root of some of the diversity related challenges that have become the subject of great discussion in recent years.

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The Changing Profile of Higher Education Students One factor driving changes in the demographic profile of college students in the US is the changing shape of the American young adult population as whole over the last few decades. Large growth-rate differences among ethnic groups in this age range signal that the nation’s population will continue to change shape for generations to come. The US Census Bureau (2002) projected that for the period 2000 to 2010 the college student population would increase by 10.9% for Asian, by 20.1% for Black; and by 35% for Hispanic, but only by 7.2% for White students. Beyond population changes, public policy shifts and internal social justice initiatives have moved institutions to grant higher education access to students from increasingly diverse backgrounds. Arum, Gamoran, and Shavit (2007), reporting on higher education transformation in 15 countries (including the United States), reported significant expansion in higher education access in the last few decades. Recently colleges and universities have begun to capitalize on emerging scholarship identifying the value of diversity on campus for all students and in contributing to broader institutional goals (Gurin, Nagda, & Lopez, 2004) to justify diversity oriented initiatives. There is a similar story to be told internationally. In the nation of South Africa for example, Kraak (2009) reports that in 1988, despite the fact that they were around 80% of the nation’s population, only 10% of students at universities in South Africa were Black. White students, about 10% of the nation’s population, occupied 77% of university slots. By 2002 these numbers had shifted dramatically2 such that 40% of the students at these institutions were Black, and only 36% White. The numbers for Coloured and Indian students have shifted similarly for the same period.3 In the Caribbean there is also evidence of progress made with regards to higher education access. Miller (2000) reported growth in enrollment after the Montego Bay Declaration of 1997, which set a goal of substantial expansion and provision of higher education across the region. Such shifts in higher education access may be signs of progress toward social justice, but bring with them challenges as well. For example, important questions need to be asked about whether higher education institutions, as they are commonly configured, are equipped to meet the needs of their newest clientele. Trends associated with changes that have already occurred give some indication that most are not. One sign that US institutions may be struggling to serve this new breed of students is that despite gross increases in enrollment in each racial category, between-group gaps in educational attainment have in fact widened or remained static over the last few decades (Karen & Dougherty, 2005; Price & Wohlford, 2005). Some educators, administrators, and policy makers are quick to ascribe the gap to student characteristics, (in particular to the stereotype that diversity initiatives have brought less capable students to campus), however other explanations may be more fitting. As mentioned, despite great changes in student

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demographics, there has been little or no change in the basic formula for how things are done in higher education settings (Hargreaves, 1980). As a result, there is a strong likelihood that institutional capabilities and services, and students’ needs have become systematically mismatched in ways that can only lead to problems for students and institutions alike. In this chapter we will argue that cultural differences in conceptions of the relationship between self, others, and institutions play a role in some diversity related challenges. We will suggest that by increasing our understanding of these issues, and if we are willing to reconsider our basic assumptions about how higher education is properly done, the disconnect between students and institutions can be addressed to the benefit of both. We will begin with an analysis of how these dynamics have evolved and will offer a model describing how they may play out differently for students of different backgrounds as they interact with institutions owing their heritage and structures to the cultures of Europe. We will next describe, as a case in point, a recently completed research project in which we examined these issues among African descended young adults of three nations, the United States, South Africa, and Barbados. Our analysis locates the problem, and with it the onus for change, primarily with institutions rather than students and in so doing suggests a need for colleges and universities to reorient resources, capacities and services. The chapter will conclude with some recommendations for higher education administrators and faculty and suggestions for further research.

Institutional Challenges In the context of higher education, a student body is “non diverse” when it is composed of primarily White Anglo-Saxon, Protestant, middle class heterosexual and depending on the field of study, men. Strictly speaking of course a great deal of some types of diversity is certain even within those narrow parameters of human identity, a fact that betrays the term’s common usage to be more political than sociologically accurate. This does not diminish the legitimacy or importance of diversity concerns (Henrich, Heine, & Norenzayan, 2010). Indeed such political designations are usually, as this one is, derived from long histories of political, social, and economic tensions between groups of people who tend to benefit from or suffer at the hand of a given social order.4 In any case the type of interest to this work is cultural and to a lesser extent, ethnic diversity. There is a growing literature that seeks to describe and address the kinds of problems faced by students of color in higher education (Altbach, Lomotey, & Rivers, 2002; Bowman & Smith, 2002; Solmon, Solmon, & Schiff, 2002), the most apparent and concerning consequence of which are academic underachievement and underattainment. Both are associated with substantial social and economic cost, such as higher unemployment, lower earnings, and greater dependence on

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welfare and other social services (Rumberger, 1987). Feagin (1992) identified feelings of alienation, isolation, and intimidation, as well as perceptions of discrimination and racism among the factors that can undermine students’ academic success. Students of color who sense that their institutions are not supportive of their personal, social, and academic needs can become alienated (Loo & Rolison, 1986) and so more likely to drop out. Combinations of these and related factors can lead students to feel unwelcome at the very institutions charged with serving them by (Altbach, Lomotey, & Rivers, 2002; Thaver, 2009) Many (though certainly not all) colleges and universities have directed significant resources toward addressing such problems (Braxton & McClendon, 2002). Some have mechanisms for early identification of students who may need academic and or psychological support. Others offer academic support directed specifically toward minority students, and or establish organizations charged with helping students from similar ethnic backgrounds create and maintain a sense of community (Bowman & Smith, 2002). Despite all of the staff, funding, and other resources that have been committed toward retaining and supporting them, problems of underperformance and poor retention persist (Kurlaender & Flores, 2005). Theory and research in social and cultural psychology invites us to wonder whether existing efforts are often founded on critical misunderstandings of the problems they hope to solve. For example, most current efforts to “help” are aimed at helping non-traditional students “fit-in” to the traditional structures of higher education institutions as if those structures are immutably and unassailably appropriate when this may not be the case (Whitla, Howard, Tuitt, Reddick, & Flanagan, 2005). While it is hard to argue with the long history of relative success enjoyed by higher education as an enterprise, it certainly also stands to reason that any industry needs to progress as its clientele evolves. For example, an increasing number of higher education enrollees come from cultures known to prioritize people and interpersonal relationships over independence and self-actualization. Known in social psychology as self-construal, status on this single variable could have important consequences for how students and institutions interact.

Self-Construal Orientation Among social psychologists, self-construal is defined as a person’s primary orientation towards significant others (Singelis & Sharkey, 1995). A distinction is made between an independent and interdependent construal of the self, where people can be thought of as disposed toward thinking of themselves as mainly independent of or interdependent with others. A person’s primary orientation toward others is known to inform his or her perceptions, attitudes, and perspectives on social relationships and social reality, and thereby to govern how they

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interact with others. People whose self-construal is independent are though to be primarily concerned with securing their own success relative to others while those who see themselves as interdependent are said to value the success of the group as a whole over their own success. Research has found that interdependent self-construal is associated with public face saving, obedience (Argyle, Henderson, Bond, Iizuka, & Contarello, 1986), embarrassability (Edelmann & McCusker; 1986), higher regard for the views of others (Modigliani, 1966), with feeling that group memberships are appropriate markers of identity, (Triandis, 1995) and to a preference for group over individual or competitive interactions (Nobles, 1974; Triandis, 2001). Independent selfconstrual is associated with feeling that possessions and accomplishment are appropriate markers of identity, with the display of ego-focused emotions (Triandis, 1995), with preference for working alone (Earley, 1993) and for placing importance on having choices (Iyengar & Lepper, 1999). It’s easy to imagine how a person’s self-construal would influence their experiences in higher education settings. Whether a student perceives him or herself as interdependent or independent would govern how s/he will interact with faculty members and other authority figures on campus. It would also influence who a student perceives to be part of his or her peer group and how they would relate with those people and others. It would also likely influence whether, with whom, and how they might seek support when they encounter difficulties in school. Through all of these things, the student’s self-construal orientation would influence their efforts to establish a place in a campus community and would have a significant impact on the types of strategies they pursue toward academic success.

How Does Culture Influence Individual Differences in Self-Construal? Increased access to higher education has brought young people from various racial, ethnic and national groups to campus in unprecedented numbers and with them patterned, culturally rooted group differences in things like self-construal. Culture is commonly defined as the shared values, norms, beliefs, and behavior (habits, traditions, rituals) that distinguish groups of people (Gordon & ArmourThomas, 1991). Contemporary scholarship in psychology increasingly distinguishes those dimensions from what has been termed cultural deep structure (Moemeka, 1998) which can be defined as the perceptual, cognitive, affective and behavioral predispositions that are common among members of cultural groups. Research indicates that there are important deep structure cultural differences in how groups of people perceive relationships with others. For example, a variety of authors have observed that the psychological orientations of people of North American and Western European heritage are largely based in individualism. By

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contrast interdependent self-construal has been associated with people of Asian, South American, Pacific Island, and African heritage (Adams, Anderson, and Adonu, 2004; Markus, Kitayama, & Heimann, 1996; Nobles, 1974; Triandis, 2001). Beyond just varying by cultural group, however, self-construal is of interest to this work because much of the tension attributed to increasing “diversity” among students may be attributable to the fact that young people from cultural groups that emphasize interdependence are showing up on campus in numbers significant enough to bring to light a mismatch between institutional and students’ orientations.

Self-Construal in Higher Education Institutions Just how much students’ own self-construal will be relevant to their educational experiences depends significantly on the position of their higher education institutions on related issues. Though it is not right to think of an institution as having a self-construal per se, it is well established that institutions generally reflect the values and priorities of those who originate them and that their structures and functioning generally serve to maintain and even enforce same. Because higher education as it exists globally today was derived largely from the template of European higher education of the late eighteenth century (Thelin, 2004), the independence and competition that were among the key value orientations of that period permeate the structure and infrastructure of higher education even today. If it is true that both students and institutions are bound to be positioned on the question of how the ideal person should relate with other people, and that they can be positioned differently, it stands to reason that a direct examination of such dynamics could be useful in understanding issues related to the fit between students and institutions. Related to the previous discussion on interdependence and independence, if a student has a strong interdependent construal of the self, and the institution is organized around the assumption that the ideal student is primarily independent and autonomous the disconnect can only lead to problems for both parties. Issues of culture are highly relevant to any discussion of diversity in higher education precisely because it is apt to be ignored. Where it is attended to, the discourse tends to center on expressive or functional definitions of culture. Moreover educators tend to view institutions as culture-free and in so doing assume that their configuration and modes of operation are the only or ideal possibilities. This assumption is generally false. Like deep structure culture among groups of people, once incorporated into the infrastructure of institutions such orientations can be determinedly self-maintaining. We can speculate on how transactions play out when a student and institution are matched in emphasizing independence. For example, students who are independently oriented would likely prefer primarily independent learning.

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Because such preferences would be consistent with most of the pedagogy to which they will be exposed and by which their grades are determined, we expect independent learning preference to have a positive relationship with academic success. We posit that institutions affirm and enforce independence beyond pedagogy and so predict that independent self-construal itself would also have a positive relationship with academic success. The dynamics would change significantly in cases where there is a mismatch between student and institutional orientations.

Self Construal in the African Diaspora The empirical literature in cultural psychology suggests that interdependence among many continental African groups is, more than an appreciation for one’s relational connection with others, experienced as an inescapable fact (Moemeka, 1998) and as a connection not only to other people but also to place, spiritual forces, and a sense of built-in order (Feme, 2001). In the broader African Diaspora, this emphasis takes related forms. South Africans of color for example have also been described as emphasizing interdependence (Eaton & Louw, 2002; Naidoo & Mahabeer, 2006), often under the name Ubuntu (Letseka, 2000). A growing body of scholarship and research places African Americans as a group closer to other groups of the broader African Diaspora than to the Euro founded mainstream of US culture on several deep-structure cultural themes including ones related to self construal (Boykin, 1986; Gaines, Ramkissoon, & Matthies, 2003; Nobles, 1974). Boykin (1986) for example describes African American culture as emphasizing communalism (Boykin & Allen, 2000), a term that describes a person’s appreciation for the fundamental interdependence among people. Communalism among African Americans is said to have origins in the West African groups from which African American people were enslaved and to have been passed across generations via home and community socialization practices. Communalism has been related positively with volunteerism (Mattis et al., 2000) religiosity, (Mattis, Hearn, & Jagers, 2002) and moral reasoning (Woods & Jagers, 2003). African American students have been shown to prefer communal over individualistic (Hurley, Allen, & Boykin, 2009; Hurley, Boykin, & Allen, 2005) or competitive (Marryshow, Hurley, Allen, Tyler, & Boykin, 2005) interactions. There is comparatively less research concerning self-construal or related variables among Caribbean people. The available findings are somewhat mixed but generally suggest that interdependence is a significant cultural theme among West Indians (Gaines et al., 2003; Hunter, 2008; Punnett, Greenidge, & and Ramsey, 2007). For example, a mix of Caribbean participants living in the region and abroad scored similarly to Africans on the communalism dimension of the Africentrism scale (Kwate, 2003).

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Cultural Discontinuity in Higher Education Settings An analysis of the interplay between race and culture can help to account for some of the more subtle ways that the status quo in higher education can undermine students of African heritage. For example, Nasir and Hand (2008) pointed out the importance of considering how race and culture interact as students constantly reconstruct their identities in interaction with individuals, communities, and social institutions. Given that interdependent individuals are known to experience the world from the perspectives of significant others (Aron, Mashek, & Aron, 2004), students who experience the exclusion and or denigration of their ways of understanding the world are bound to notice similar struggles among others who look like them. They may come to interpret these experiences, and so higher education itself, as negatively racialized. Since higher education is voluntary, disengagement also seem likely as a coping strategy, however for those who persist, coping with this shared experience would likely galvanize their group (racial) identity and reinforce the attitude and behavioral mores shared among them.

A Limited Model and Exploratory Investigation Although the available previous work establishes that many of the key elements are in place to support our assertion that issues of culture play a significant role in the challenge of serving an increasingly diverse clientele, missing is an empirical examination of the specific way in which group differences in self-construal may interact with various academic and social transactions that take place on a college campus. Figure 3.1 illustrates a model that we propose describes relationships among a few of the cultural and racial variables that should contribute to students’ prospects for success in higher education. The model is narrow and does not intend to represent the entire array of relevant variables. Rather, by describing the relationships among a few key variables, we hope to support the argument we have made and to provide a template for examining other relevant variables. Examining the model closely, first we have argued that many African descended young adults are socialized toward interdependent self-construal. The question at hand then is, what role being interdependently orientated is likely to play in their academic lives at institutions that privilege and enforce individualistic and competitive attitudes and behavior in the variety of settings that make up a campus climate. We can expect a few things. It stands to reason that people high in interdependence will prefer collaborative work and may possess a repertoire of related skills that could, but seldom will be leveraged to their educational benefit. This is represented as the positive relationship between self-construal and learning preference.

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Expected relationships in institutions organized to prioritize independent and competitive orientations Learning Preference: Interdependent

Academic Success

Interdependent self-construal Group Identity Racial

Discrimination Experience

Mistrust for Institutions

Figure 3.1 Predicted relationships among the variables of the model.

Such students could find themselves at odds with the priorities set by and enforced in the day-to-day operations of higher education in and out of the classroom. This could undermine their feelings of belonging and support on campus as they are held responsible for navigating the extra hurdles of adapting to the modes that are available to them. Therefore, we assume a negative relationship between interdependent learning preference and academic achievement, if only because the effort to adapt is added to the effort they must exert toward mastering course content. Some students will compensate by working twice as hard, but conceptually we can view communal learning preference as contributing negatively to academic achievement under these circumstances. The same should be true of interdependent self-construal itself for similar reasons relating to students’ need to navigate the broader institutional environment. Although such dynamics constitute a significant problem in their own right, they are only part of the story. As discussed earlier, additional issues arise because students are apt to notice that others who look like them face similar challenges that others do not. Coupled with the likelihood they will also witness/experience more explicit forms of discrimination,5 many such students may come to see their academic and other

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difficulties on campus in personally relevant racial terms. This is reflected in the model as a positive relationship between interdependent orientation and the belief that one’s racial group is the target of discrimination. The perception that one’s group is a target of discrimination can be expected to have a generally negative relationship with academic success. Interdependence should be positively related with racial group identity, as will be the perception of racial discrimination. We view increased racial group identity as in part a coping strategy in so much as students may provide one another a reference point and support in their shared struggles, thus we expect group identity to have a positively correlation with academic success. Finally, we expect that as a protective factor group identity would mediate the negative relationship between interdependence and academic success.

The Current Study In a recent investigation we set out to test several of the assertions of this model. First, we wanted to determine whether we would find evidence of group orientation among young adults of African heritage in three nations. Second, we wanted to examine the relationship between students’ self-construal and their learning preferences. It is safe to assume that by young adulthood they have in some ways acclimatized to the individualistic and competitive priorities typical in educational contexts, thus it is important to determine whether students who may be group oriented in their personal lives, will prefer interdependence-oriented learning. Finally, we determine whether independent self-construal would be associated with variables related to race. We chose three variables, feeling that one’s group is discriminated against, feeling connected with others of their racial/ ethnic groups, and with mistrust for people of European heritage. The third question could shed some light on whether a simple orientation mismatch between a given student and their schooling experiences is likely to take on larger connotations related to race. To accomplish this study we measured orientations toward others, learning preferences, perceived discrimination and cultural mistrust among three samples of African descended young adults in South Africa, the United States and Barbados. Participants were 191 African descended male and female young adults. For the purposes of this chapter we present an abridged description of the methods, analysis, and results.

Measures We assessed participants’ group orientation using the self-construal scale (Singelis & Sharkey, 1995) the communalism scale (Boykin, Jagers, Ellison & Albury, 1997)

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and the The Inclusion of Others in the Self scale IOS (Aron, Aron, & Smollan, 1992). The Self-construal scale is a 24-item measure with subscales assessing respondents’ construal of the self in relation to others. The scale yields separate scores for interdependence and independence. The communalism scale is a 31item self-report measure that assesses an individual’s orientation towards social bonds and interdependence. The IOS assesses “respondents’ perception of overlap between themselves and a target other,” in this case other members of their racial/ ethnic group (Li, 2002; Mashek, Cannaday, & Tangney, 2007). This single-item measure presents a series of ten two-circle Venn diagrams. In each pair the circles vary from not touching and spaced apart to almost completely overlapping. Participants are asked to circle the pair whose positioning best describes their relationship with the target other. We assessed students’ learning preferences using the communal scenario of the Learning Contexts Scenarios—college version (LCS-C). The LCS were developed to determine whether students’ attitudes toward high achieving peers vary depending on the cultural orientation expressed in the peers’ achievement attitudes and behaviors (Marryshow et al., 2005). Respondents were also asked to circle yes or no in response to a single item query about whether they belong to a racial or ethnic group that is or has been a target of discrimination in their nation of origin. Finally we administered the cultural mistrust inventory, a 48-item questionnaire that assesses the degree to which people of African heritage should mistrust and/or be suspicious of the motives and behavior of people of European descent (Terrell & Terrell, 1981).

Procedures All instruments were distributed as a questionnaire, which participants completed anonymously after giving informed consent. Participants read the instructions and items to themselves.

Results and Discussion Table 3.1 displays means for each measure for the whole sample and by group. The mean scores for the interdependence subscale of the self-construal scale was identical to the mean reported by the scale’s authors in a sample in mixed heritage college students (Singelis, 1994). Note that separated by group, mean interdependence scores for South African and African American members of our sample were slightly higher while those for our Barbadian participants were marginally lower than those reported by the scale’s authors. Mean scores on the independence subscale of the self-construal scale for the whole sample were higher than the sample mean reported by the scale’s authors. Separated by group, South African members of our sample scored higher in independence than did Barbadian and African American participants. Communalism scores for members of our sample

62 Table 3.1

Hurley & Hurley Means for self-construal and communalism Nationality

Interdependent Self-construal Independent Self-construal Communalism

mean SD mean SD mean SD

AA

SA

BBDS

Tot

4.12 a .63 4.41 a .66 4.24a .51

4.22 b .85 4.77a , b .60 4.22 .47

3.89a , b .66 4.40 b .66 3.98a .62

4.01 .68 4.44 .74 4.11 4.06

4.01 Singelis, 1994 4.02 Singelis, 1994 4.31 Boykin et al., 1997

Notes: Means in the same row that share a subscript letter are significantly different from one another.  p < .05,  p < .01.  In an ethnically diverse sample of college students. Means have been converted to enable comparison with the six choice response format used in this study.  In 3 samples of African American college students.

were somewhat lower than means reported by the scale’s authors among African American college students (Boykin, Allen, Davis, & Senior, 1997). By group, the mean for African American and South African participants was close to those reported by Boykin, Allen, et al., while that for Barbadian participants was lower. Mean Inclusion of Others in the Self (IOS-Eth) ratings made by Barbadian participants overlapped significantly less than those chosen by South African and US participants. Table 3.2 displays the difference. As expected, all three groups of Table 3.2

Means for IOS-Eth, LCS-C, Discrimination and Cultural mistrust Nationality AA

Inclusion of racial/ethnic group in the self Communal learning preference Individualistic learning preference Competitive learning preference My racial/ethnic group is discriminated against Cultural mistrust

SA

BBDS 

Tot 

mean

4.65 a

5.39 b 3.85 a , b

4.28

SD mean SD mean SD mean SD Yes

1.67 4.98 .84 3.73 1.41 4.00 1.24 85%

1.78 5.37 1.1 4.33 a 1.61 4.23 1.60 67%

1.96 5.09 1.0 3.30 a 1.49 3.73 1.51 36%

1.90 5.08 .97 3.56 1.5 3.88 1.43 58%

mean SD

3.29 a , b 2.82 a .53 .33

2.93 b .66

3.06 .62

Notes: Means in the same row that share a subscript letter are significantly different from one another.  p < .05,  p < .01

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our sample preferred the communal over the individualistic and competitive peers. The three groups did not differ significantly in their ratings for the communal peer. Fifty-eight percent of respondents answered yes to the single item asking whether they are part of group that is the target of racial/ethnic discrimination. The proportion varied by group with 85, 67 and 36% of respondents answering yes among the African Americans, South Africans and Barbadians respectively. Finally the mean for cultural mistrust was right at the midpoint between strongly agree and strongly disagree indicating that overall about as many of our participants expressed considerable mistrust for people of European heritage as did not. By group, African American participants expressed significantly more cultural mistrust than did participants from the other two groups.

Testing the Model We examined the pattern of relationships among the variables of interests as a partial test for the predictions of our model. For this test we used participants’ scores on the communalism scale as the indicator of their group orientation.6 Figure 3.2 displays the observed relationships. As expected, communalism was a positive predictor of participant’s liking for the communally oriented peer

Observed relationships Learning preference:

Communal

Academic Success

Group orientation:

Communalism Inclusion of the

GROUP In the self

Perceived

Discrimination

Cultural Mistrust

Figure 3.2 Observed relationships among variables of the model.

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(path a) but unrelated to their liking for the individualistic or competitive peers (not pictured). Our observation of this relationship in this age group speaks to the importance of understanding and accounting for students’ home- and community-based orientations in the ways that we conceive and enact pedagogical and support strategies that hope to serve them. An issue that has not been empirically examined in a young adult (or any) sample is how a disconnect between their orientations and those supported in the institutions of higher education they attend may lead students to interpret their experiences in racial/ethnic terms. Communalism scores were positive predictors of the overlap our participants reported on the IOS-eth measure (path b). Thus, the more group oriented were our participants, the more they included other members of their ethnic group in their own self-concept. This observation is significant because although communalism scores did not vary systematically between those who did or did not believe that their ethnic group is a target of discrimination, IOS-Eth scores did. They were higher among participants who responded yes to that item (path c). Those who reported being discriminated against also scored higher in cultural mistrust than did those who did not (path d). There was also no relationship between communalism and cultural mistrust in this sample. Interestingly among those who did not see themselves as members of a group that is discriminated against, communalism was associated with feeling less cultural mistrust (not shown). This observation is consistent with our contention that interdependence in and of itself would not be negatively related to intergroup relations (perhaps even contributing positively), but can become racialized under some conditions. Our exploration of the model affirms our hypotheses about the relationship among the variables for which we have data but the investigation omits the critically important outcome variable—academic achievement—which the model hopes to predict. We did not set out to conduct a comprehensive test of the model but to take an important step forward in establishing relationships among variables that are highly relevant to but not commonly considered in the discourse concerning diversity and academic achievement in higher education settings. The pattern of relationships allows us to piece together some picture of the kinds of difficulties that such students might face in higher education settings. After gaining access to institutions of higher education students may arrive and find themselves facing unexpected and unnecessary extra hurdles in the classroom as pedagogical strategies are employed which fail to capitalize on their existing skills and orientations to learning. Students may find their institution unprepared or unwilling to support their ways of understanding and interacting with the world and so be frustrated in their attempts to seek assistance with classroom and other difficulties. Finally they may observe that others like them face similar difficulties and are similarly unsupported by the institutional infrastructure and so may conclude that the institution does not make a priority of their progress or value

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their presence on campus. This may be consistent with their perception that people of their group are the targets of systematic discrimination and confirm and enhance their existing mistrust of mainstream institutions. Surely our data only provide indirect supporting for the assertions of the model described here. Further research will be needed in order to confirm the relationships we have observed, test our assertions about the relationship between these variables with academic achievement and to determine the generalizability of this model (in whole or in part) to students from other heritage groups known to prioritize interdependence. We also hope that this work will encourage research on the relevance of other cultural variables to higher education. Bearing those things in mind in the next section we next offer a few categories of suggestions based on our analysis that may be helpful to faculty and administrators.

Recommendations As discussed in the chapter group orientation may inform an individual’s interactions with others in various settings. Of particular interest to us is how group orientation would relate to students’ experience at institutions of higher education. Some interesting findings emerged that could yield insight into how students of color may experience these institutions.

Pedagogy (i) We observed a positive correlation between communalism and a preference for the communal learning style. This suggests that for our sample, those students who scored high on communalism also showed a preference for the communal learner. This finding may inform teaching pedagogy, at least to the extent that instructors should be aware of the fact that differences exist in how students choose to learn. Interdependence was also positively correlated with a communal learning preference. This indicates that when given a choice, those students who reported high levels of interdependence also showed a preference for communal learning. Braxton and McClendon (2002) suggest that, as part of faculty development, cooperative/collaborative teaching techniques should be the focus in faculty development workshops and seminars. Understanding and accommodating communal or collaborative learning and teaching may enhance the learning experience of students who prefer the communal learning mode. It is not suggested that all teaching should happen in a communal or cooperative mode, but that instructors should develop an understanding of the different orientations students may bring to the classroom, and seek ways to accommodate these preferences. (ii) Johnson and Johnson (1994) have done extensive work investigating the conditions under which cooperative learning is most successful. These conditions

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can help enhance both the teaching and learning experience of students. The most important factors that contribute to effective cooperative learning, according to Johnson and Johnson, include positive interdependence (team members perceive that they need each other to complete the task); individual accountability (an understanding of each member’s contribution to the task); face to face interaction (opportunities for members to give advice to other members on how to complete the task); interpersonal and small group skills (the ability for group members to interact and communicate with each other); and group processing (opportunities for group members to assess progress and set new goals if needed). These factors make it clear that for the most part, successful cooperative learning depends on the personal attributes of the students, and their ability to effectively negotiate and plan with other students with whom they are working.

Institutional Climate (i) In our sample the question related to whether participants feel they have personally experienced racial/ethnic discrimination yielded interesting results. First, a positive correlation was observed between communalism and also indicating that “one belongs to a group who has been discriminated against.” Also, 85% of African American, 67% of South African and 36% of Barbadian participants indicated that they belong to a group whom they believe has been discriminated against. Institutions are therefore faced with the challenge of creating a safe environment for these students who have become to the institution aware of different forms of discrimination imposed by the broader society. Pope and Reynolds (1997), among others, tried to advise institutions on how to create an academic environment that supports minority students. They argue that a key goal of student affairs on campus should be to work toward creating a multicultural campus environment. It is proposed that this is best achieved by dedicating significant time, attention, and resources to creating a campus that is open to all cultures. Reynolds and Pope propose that this is best achieved by developing an inclusive mission statement, extensive recruitment and retention efforts, strict anti-discrimination policies, and activities and curricula that create an awareness and celebration of diversity, By being vocal about an institutional commitment to being inclusive, institutions may succeed in assuring these students who believe that they belong to groups who have been discriminated against, that they are welcomed and supported at the institutional level. (ii) Credle and Dean (1991) and Fleming (1984) point out that it is not just the responsibility of student affairs to create an inclusive institutional environment, but that institutions should recognize the special needs of minority students. They propose, among other things, that institutions recognize and respond to the affective and social development of students, provide mentoring opportunities,

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assist with academic support, provide guidance through organizational structure and provide proper assistance in career choices.

Conclusion We have argued that much of what has been coded as growing diversity on higher education campuses actually amounts to increasing participation by young people whose natal socialization experiences have lead them to be high in interdependence. This mismatch is certainly not the only problem related to diversity but it is one that is broad enough and important enough to be addressed systematically and to substantial benefit. Moreover it is a problem primarily because of reticence on the part of universities to reexamine their ways of doing things. Accepting that individualism and competition in the institutional culture of higher education are cultural artifacts more than they are meaningful pedagogical strategies is important. Even if we decide they are meaningful educational goals, there is no reasonable argument to be made that students should be forced to assimilate without help. To the extent that those who have the power influence and construct the day-to-day realities of higher education do inflexibly expect students to assimilate and give no deference to who they are or what they bring to the table, we are choosing not to value their ways of being. To the extent that the higher education institutions that are meant to serve them when we as educators behave as if they only deserve the fruits of education when and if they conform to our expectations we are choosing not to see them and in so doing diminishing our own reflection of the world.

Notes 1. Rich, Adrienne 1986 “Invisibility in Academe.” 2. Progress toward diversity in South African higher education institutions is obviously in large part due to the end of Apartheid in 1994, however, within the larger societal changes, higher education institutions have had to negotiate the implementation of new policies on their campuses. This is evidenced by the fact that the diversity statistics vary dramatically among even the most visible universities nationally. 3. We should note that the referenced racial categories were established by the Population Registration Act of 1953 (Sehoole, 2005). An individual was classified Black if s/he was from one of the original Southern African tribes. The term Coloured originally referred to individuals of mixed heritage, though Colored people in South Africa do not consider themselves bi-racial or mixed, but members of an established and distinct culture that has evolved over 150 years. Individuals who are classified Indian are generally of Southern Asian heritage, and the White population is descended from mainly Dutch and British settlers.

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4. It is possible to criticize the language of diversity, which is often aside from the goals of those who champion it (to address past and current ills in the social structure that give people different access to the levers of social mobility). I believe the gentle language of diversity is intended to soften the discussion by removing guilt and blame terminology that accompany the language of social justice movements. There is great value in using language that invites into the conversations those who have the power to influence events but who may also be responsible for, complicit with, or who at least benefit from, unequal social orders. Unfortunately the gentler language can also lend itself to distortion and overgeneralization and misdirection to the point of diminished usefulness. 5. The second author teaches a course in Black Psychology and in 10 years of teaching has never taught the course in a semester in which there was not some campus, national, or global racial event controversial enough to warrant putting aside the syllabus in favor of a discussion. 6. Communalism scores were moderately correlated (.48 p < .000) with participants’ scores on the interdependence subscale of the self construal measure and showed better psychometric performance (were more reliable in the whole sample and for each group, and showed better discriminant and convergent validity) in this sample.

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4 Unintentional Prejudice and Social Psychology’s Lessons for Cross-Racial Teaching Elliott D. Hammer

Research on interracial interactions shows that unintentional prejudice affects our judgments and behaviors more than most of us assume. Unintentional prejudice is an insidious process by which society’s unwritten rules about status, respect, and worth can elude even the most egalitarian among us. As such, it is na€ıve and inappropriate for any of us to assume that we are immune to the effects of a prejudicial society. As educators, with so much contact with a variety of students, we must be especially vigilant against bias in our perceptions and treatment of our students. As I will argue, students who are a minority in the classroom likely experience a sense of “other”ness that prevents them from feeling like full members of the class. We are wise to engage in a bit of affirmative action (in the true intent of the term) in the classroom and apply extra effort to make sure that students from racial minority groups don’t fall through the cracks. I will discuss some of the processes by which stereotypes can affect us in order to specify the path that such influences might take, thus enabling us to maximize our positive impact on our students.

Unintentional Prejudice The recent arrest of African American Harvard professor Henry Louis Gates, after he was reported trying to open a stuck door at his own house in Cambridge, provides an object lesson in the ways in which some people are treated differently

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because of their race. By all accounts, the arresting officer, James Crowley, is no prototypical racist. He had conducted diversity training workshops, his actions in the arrest have been supported by his African American superior, and some have noted that he administered mouth-to-mouth resuscitation to the Black basketball player Reggie Lewis (Sweet, Szaniszlo, Crimaldi, Van Sack, & Dwinell, 2009). Crowley defended himself as non-racist, and it is clear that he is not the stereotypical power-abusing policeman out to deprive a Black man of his rights. And yet, one may question whether Officer Crowley’s interpretations of and reactions to Gates’ behavior would have been different if Gates had been White. For example, would the officer have responded differently if the neighborhood had more Black residents? We might further speculate as to whether the passerby who reported the apparent break-in would have regarded Gates’ behavior as suspicious if the two people attempting to shoulder open the door had been White, or even whether Gates’ response itself would have varied if he were not Black. Obviously, we cannot know the answers to these hypotheticals, but as educators, we must summon the energy to analyze our own behaviors and ask whether we might be letting biases cloud our judgments and harm our relationships with our students. Are we, for example, allowing subtle inductions of our stereotypes to sway our expectations and treatment of students of color? Like Crowley, we consider ourselves unprejudiced, but also like Crowley, we are subject to societal cues and to the limitations of our own capacity to be objective and fair. Indeed, overriding stereotypes requires energy. Although we might be inclined to think of stereotypes as the sole property of true bigots, Patricia Devine’s (1989) work established that high- and low-prejudiced people are equally aware of the stereotypes of members of various groups within their society. Thus, it appears that stereotypes are “in the air,” as it were (Steele, 1997), and that individuals either accept those stereotypes, or they apply the energy necessary to overcome or circumvent them, and to individuate others. However, as Gilbert, Pelham, and Krull’s (1988) research shows us, we may not always be able to overcome those stereotypes; we may be so distracted, tired, or otherwise cognitively taxed that we are forced to rely on the convenience of energy-saving stereotypes for our judgments. Considering these assertions together (Hammer, 1996), it stands to reason that Officer Crowley, although not a racist by disposition, may have been influenced by stereotypes when assessing and responding to the situation at Gates’ house. It is also reasonable to expect that educators, with so many different things to think about—difficult students, pesky administrators, looming deadlines— occasionally succumb to the influence of stereotypes in our society. Indeed, the mere presence of a member of a particular group serves to activate stereotypes of that group (Mason, Cloutier, & Macrae, 2006), even those with which we disagree or find objectionable. If we are not focused on suppressing those, then they may prevail over our better judgment.

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Understanding Our Students’ Experiences I frequently conduct exercises in my social psychology courses in which I have students reflect on times that they may have been the victims of prejudice. My goal in these exercises is to illustrate how we use certain cues in an interaction to identify prejudice as the cause for someone else’s behavior, or as evidence that there is another reason for how someone treats them. Despite my intentions to discuss the ambiguities of potentially prejudiced behavior, I inevitably have a number of students whose examples are anything but ambiguous. Frequently, students’ examples of when they “may” have been the victim of prejudice were when a customer where they work, or a neighbor, or a colleague, referred to them with a traditional and offensive racial slur. My intent is to hash out what someone’s actions toward the student “may” have meant, and I find that the actor was so blatant as to remove any debate from our situation. It makes for a less helpful discussion as far as the concepts we are to discuss is concerned, but it is always revealing that despite psychology’s focus on subtle or even debatable signs of prejudice, students of color are still experiencing unambiguous prejudice on a regular basis. I mention these observations because as we work to meet our students where they are to help them along in their education, we need a clearer idea of where exactly that is. Not everyone whom our students encounter is happy that the Jim Crow era is over, and they let our students know. Even though overt racism may be more prevalent in the South, many students from the North experience racism from Whites there who see themselves as less racist and therefore immune to prejudice. Our students have a wide array of experiences, and we must avoid the tendency to see them as a homogeneous group, even as we seek to appreciate the differences between them and us. Obviously, such a sensitive goal is not easy to accomplish, and an abundance of research shows us why we may not always be up to the task. Consider, for example, the classic work of Word, Zanna, and Cooper (1974), who demonstrated the self-fulfilling prophecies that may result from differential immediacy behaviors directed at people of different races. In one study, the researchers noticed that although Black mock-job-applicants performed more poorly than White ones, the interviewers (who were White) also treated the two groups of “interviewees” differently. White applicants received more encouraging non-verbal behaviors, such as a more open posture and more time to respond to a question. So in a followup study, Word et al. used only White interviewees, but they manipulated the non-verbal behavior of the interviewers. They found that White applicants who received the non-verbal signals normally directed at Black applicants also performed poorly in the interview. Thus, there was a difference in performance, but the behavior of the interviewer brought it about. Likewise, it is reasonable to assume that our own treatment of our students, intended to be benign, occasionally elicits inferior performance.

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We can easily imagine a classroom scenario in which a White professor, despite being explicitly committed to equal treatment of her students, unconsciously exhibits more encouraging and accepting nonverbal behavior toward her White students. Or, imagine the professor who unconsciously assumes that the Black student who wrote the essay he is grading at 3:00 a.m. has less potential, so he notices its flaws more quickly than he would a White student’s. These White students subsequently gain the benefits of confidence and more constructive feedback than do Black students, who received less positive nonverbal expressions and less constructive feedback. It is our responsibility as educators to examine closely and critically what behaviors on our part may have elicited differences in performance or simply in the grade. So what is the well-meaning professor to do? I suggest, on the basis of qualitative and quantitative data collected from students, as well as 14 years of teaching as a White professor in predominantly African American classrooms, a number of considerations that educators should attend to in their work. Obviously, vigilance and self-critique are important factors, but there are some specific perspectives and techniques of which faculty should be aware. My hope is that although most readers are not in my situation, as the White minority in the classroom, all educators can create a classroom environment in which ethnic minority students, whether they represent 1%, 10%, 50%, or 100% of the class, can share the most beneficial learning experience possible.

Toward a More Systematic Understanding of Our Students’ Perspectives It may be helpful for the interpretation of these data to understand the nature of my institution and its students. Xavier University of Louisiana, founded in 1925, is the only historically Black college or university (HBCU) that is also Catholic in the United States. Its foundress, St. Katharine Drexel, devoted a considerable inherited fortune to educating Black and American Indian students and was canonized in 2000. The university has earned a strong reputation largely as a result of its premedical programs and its school of pharmacy. Despite currently enrolling only 3200 students, Xavier has, for 15 years, led the country in the number of African Americans going on to medical school, and it is among the top three producers of African American doctors of pharmacy. Because of its favorable reputation, Xavier attracts some exceptionally well-qualified students. Because of its mission to give students who may come from more deprived backgrounds a chance to reach their potential, Xavier also admits a number of students who are not well prepared for college. As a result, classes contain students from a wide range of backgrounds and preparation levels.

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My full-time career in academia began in 1996 at Tennessee State University, another HBCU in Nashville. I was there until 2000, when I came to Xavier. Because I had been a White professor at two HBCUs, I began to view my—and my students’— campus and classroom experiences through the lens offered by my training as a social psychologist. Although most HBCUs have large numbers of White faculty, my background in social psychology provided a set of tools to systematically examine some of the issues that confront Black students and to better understand their relationships with their Black and White professors. To this end, I conducted a series of studies, inviting students to complete surveys and participate in semi-structured discussions with either me or an advanced undergraduate student who was more similar than I to our participants’ demographic. The responses she received were very similar to those I received, so I will generalize across responses in discussing what we learned. All of the students who responded identified themselves as at least partly Black or African American (although some described themselves as Creole).

Quantitative Responses Sixty-two students, representing a range of years in college and majors, completed 13 Likert-type items and a couple open-ended free-response items asking about their perceptions of their Black and White professors. The Likert-type items and corresponding descriptive data appear in Table 4.1. The response patterns from the Likert-type items were generally consistent with the points made in both the survey-based free response questions and the open discussion sessions. In general, students do not express much displeasure at having White professors, although they wish to some degree that they had more Black professors. We explored this theme more fully in the discussion groups. These discussion groups indicated that the type of bond students feel with their Black professors is somewhat different from that with their White professors. Some of this reported discrepancy in type of bond may shed light on the relatively neutral average response to the third Likert item, which asked whether White professors are concerned about issues such as discrimination. These responses suggest that White faculty need to do more to connect with their students of color, who are generally accepting of the White faculty members’ presence and expertise. These quantitative data do more to verify the potential presence of a race-based problem than provide solutions to that problem. The bulk of the remainder of this chapter, therefore, cites and explores the qualitative responses students gave on both the survey-based free-response questions and during the group discussions; importantly, strong, consistent patterns emerged. After highlighting representative comments from students, I discuss social psychology’s relevance to making sense of these comments and then offer concrete suggestions, derived from these data and relevant theories, for enhancing our students’ experiences.

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Table 4.1 Descriptive quantitative data regarding questionnaire items (1–7 scale, 1 ¼ strongly disagree; 7 ¼ strongly agree) Item

Mean

SD

Range

Mode

White professors are equally able to teach to Black students as Black professors are. I have learned more from my Black professors than from my White professors. My White professors are concerned about issues, such as discrimination, that affect Black students. I would like to have more Black professors at Xavier. My White professors can relate to me as well as my Black professors can. An HBCU has a special responsibility to employ Black professors. White professors are better qualified than Black professors. White professors have no business teaching at an HBCU. White professors should be able to teach courses such as African American Studies and Black Psychology. I am more comfortable with Black professors than with White professors. I sometimes worry that my White professors are prejudiced against me. White professors have a special responsibility to inform themselves about Black culture. My Black professors have a special interest in seeing me succeed that my White professors don’t share.

5.77

1.14

1–7

7

2.98

1.37

1–7

1

4.75

1.14

1–7

5

5.43 4.18

1.29 1.20

3–7 1–7

7 4

4.31

1.50

1–7

5

1.23

0.41

1–5

1

1.45

0.73

1–5

1

3.61

1.53

1–7

4

3.54

1.26

1–7

4

3.07

1.45

1–7

1

4.87

1.25

1–7

5

3.38

1.66

1–7

1

Why Do Students Choose to Attend An HBCU? I wanted to know what made our participants choose Xavier in particular and an HBCU in general. Race played a central role in many students’ decisions to attend Xavier, for two rather complementary reasons. First, many of our students had always attended predominantly Black schools and did not want to change that pattern; they felt more secure and as though they would receive the help and support that they needed. A common sentiment is represented here: “I’ve only attended all-black schools, so that’s where I’m most comfortable. I would be too uncomfortable at a place where I was in the extreme minority and I felt that might hinder my success.” Second, many students had attended predominantly White schools or lived in mostly White neighborhoods and wanted to experience the comfort that comes with being the majority. For example, one student said,

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“My entire life, I attended school with mixed composition or majority white, so I wanted a change.” Another commented, “I’ve always attended predominantly white schools where I have sometimes felt as thought I was outnumbered and I wanted to attend an HBCU to get a different perspective and environment.” These students seem to have anticipated their need for attention and support from people at their university. As their comments show, students are keenly aware of some of the struggles they face in college, and they search for solutions. One of our jobs as faculty is to help them find solutions, and social psychology research can help us recognize both the importance and challenges of doing so. The job of professor involves a number of different components, which we must juggle and prioritize. As a result of general fatigue or frustration, we are likely to become impatient with students, to make assumptions about their capabilities. We also might allow dissatisfaction to compound as we grade a stack of mediocre papers or deal with a classroom of less-than-prepared students. At these times, we may be said to have become ego depleted (e.g., Baumeister, Muraven, & Tice, 2000), an experience that can be exacerbated in interracial interactions. The notion of ego depletion builds from the assumption that our ability to regulate ourselves—our self-regulatory capacity—is akin to a muscle, and that muscle can become exhausted over time and lose its strength. Everyone has experienced ego depletion at various times, such as when we might displace frustration from a difficult day onto our spouse or say something we regret after hours of stifling the urge to respond to a rude colleague. After excessive efforts to regulate our impulses, we become fatigued and slip. The muscle needs to rest in order to re-energize, so we might find the opportunity to take a break from someone or a stressful situation, recognizing that we may be approaching a point of no return. Certainly, our students present a unique self-regulation challenge and opportunity. We are aware that, as tempting as it might be, we can’t lash out at frustrating students, and we can’t punish current students for the sins of the former. Our commitment to fair and civilized treatment of our students sounds great when we are rested, but it can become strained under conditions of ego depletion. Self-regulation takes considerable cognitive energy, and when we are already stressed by our work or home lives, we may not be able to rally the resources to check our emotions or to resist a regrettable outburst. Not only do we want to avoid the trouble we are likely to encounter if we say or do something inappropriate, but we also want to model responsible, sober behavior that we hope our students will adopt. But being on our best behavior and treating students appropriately at all times is a challenge regarding any interaction partners, but, as abundant research shows, it is especially taxing in interracial interactions. Jennifer Richeson and Nicole Shelton have been at the forefront of applying the concepts of ego depletion and self-regulation to research on interracial interaction (e.g., Richeson & Shelton, 2003; Shelton & Richeson, 2006). In their research, they

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use the classic Stroop task in order to measure ego depletion. In the Stroop, participants encounter color words, such as black or green. These words are written in either the congruent color (e.g., green written in green letters) or an incongruent color (e.g., green written in black). Typically, participants respond slower to the incongruent words because they must override the reading automaticity effect (the automatic tendency to read any text to which we are exposed), and doing so requires an extra step in one’s processing. Still, most people are perfectly capable of doing so, with a bit of effort. If, however, one has become ego depleted, such as through excessive self-regulation, then the task of overriding that impulse becomes more difficult, and the discrepancy between responses to congruent and incongruent trials becomes greater. In a typical Richeson and Shelton paradigm, White participants engage in either an intraracial or an interracial interaction, after which they complete the Stroop task. Richeson and Shelton consistently find that participants show greater levels of ego depletion after interracial interactions than after intraracial ones, as evidenced by hampered ability to complete the Stroop task. That is, they are slower to respond to incongruent colors after interracial interactions. Why would this be? They reason is that a White person engaging in an interracial interaction is usually engaged in self-regulation, so as to avoid saying anything potentially racist or otherwise offensive. This effort is especially taxing for people who value egalitarian ideals, but anyone who wants not to come across in a markedly negative light would be motivated to invest the energy to avoid appearing racist. As a result, these participants have fewer resources on hand for the Stroop task and show decreased performance. Research on ego depletion, coupled with students’ indications that they are hoping for a supportive and positive environment, suggest that educators need to be mindful of their limits and to recognize when they are losing patience. Such cues may be less obvious in more challenging interracial interactions, but they are even more important. It is our responsibility to know when to step back from what we are doing, take a break, and reassess our treatment of our students. Recognizing the limitations of our own capacity for fair treatment is an important component of providing the support our students need.

What should White Professors Know About Black Students’ Experiences? When asked what White professors should know about Black students’ experiences, my students made it clear that they resent being seen as representing some sort of universal “Black perspective.” In fact, one reason some students chose to come to an HBCU (extending from the question above) is that they were looking for a chance to escape the spotlight. As the majority on campus, Black students do not stand out for their race. This notion reiterates a theme of White privilege in that no one asks for the “White perspective,” but many students have had the

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experience of being asked to represent the millions of African Americans in classroom discussion. Even the professor who means well by trying to bring someone into the discussion may exacerbate this discomfort. Another common theme emerged in responses to this question: Black students see themselves as having to confront more problems and complications to their education than White students. Although this may not always be true, it is a common perception and, given economic and education disparities among races, often an accurate one. One student noted that he or she wanted White professors to recognize, “the challenges we face every day for being different. The harassment and embarrassment that other ethnicities cause us on a day-to-day basis. . . . How it feels to be called n




even though slavery is over.” Such statements clearly convey the ongoing racism that many students encounter. Additionally, students invite the discussion of controversial topics such as race. Obviously, this requires a certain level of comfort on the part of the professor, but pushing those boundaries can open up the classroom, and admitting one’s own discomfort may humanize us in a situation that is uncomfortable for students as well. One student directly noted that, “being a black student is hard because you are constantly trying to beat a stereotype that black people are lazy and not doing anything with their lives.” Another said that, “being black is no longer a race or breed. It is a full-time job.” Such responses indicate an awareness—even if students aren’t familiar with the term—of the notion of stereotype threat. As Steele and Aronson (1995) originally demonstrated, and many others (see Nguyen & Ryan, 2008 for a meta-analysis) have verified, academically identified minority students can be adversely affected by the pressure to succeed. In an effort to examine the roots of test score discrepancies among racial groups, these researchers have manipulated the information disclosed to participants about a task such as academic or athletic performance. When told that a task is diagnostic of their actual ability to perform it, members of the group stereotyped to do poorly in that domain actually perform worse than when they are told that the task is unrelated to their abilities. As a result, non-diagnostic instructions can greatly reduce the racial discrepancies in performance. Social psychologists now have a pretty good idea of the mechanism behind this phenomenon. Because of students’ awareness of stereotypes predicting their failure, they face not only the desire to perform for the opportunities that excellence affords, but also the fear that failure will reinforce society’s negative stereotypes of their ability. This added burden is a distraction and can lead to impaired performance. The instructor in the classroom should take steps to try to reduce the effects of stereotype threat, as even the presence of an other-race person (Marx & Goff, 2005) or priming one’s race or sex can activate the threat (McGlone & Aronson, 2006; Shih, Pittinsky, & Ambady, 1999). For example, instructors should try to create a reduced-stress testing environment, which would benefit all students. It would also be wise to avoid comments or jokes that might raise the

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salience of a student’s racial background. Furthermore, we can work with students to help shape their own theories of intelligence to be more malleable; belief in the possibility of improvement can ameliorate the effect (Aronson, Fried, & Good, 2000; Good, Aronson, & Inzlicht, 2003). For example, basing feedback on the student’s potential to succeed on a task is more beneficial than simply evaluating present performance. Students benefit when we give suggestions for future improvement, rather than emphasizing past inadequacy. Tempering criticism with questions for consideration and suggestions for improvement can create a tone of future success. Recently, Aronson (2009) noted that how we present an exam can impact students’ grades. Emphasizing that the exam is really an assessment of the teacher—how well he or she has helped students learn the material—results in higher performance than making it a referendum on the students themselves. When students believe that a task is one on which they can improve, they do better. These factors may be especially important with students of color, who are confronted with stereotypes that their performance is inferior and that there is nothing they can do about it. Although test anxiety will always be present, we can tap into and raise students’ true abilities by alleviating this anxiety with an environment conducive to their success. Still, some faculty may notice that their default inclinations may contain biases. As Monteith, Sherman, and Devine (1998) and others have shown, some degree of effort to suppress prejudiced impulses can be beneficial in treating others fairly. Also, one of the tenets of the concept of ego depletion (mentioned in the preceding section) is that the ego is a muscle—although it can become exhausted, it grows stronger with repeated use. One student responded to the question of what they wanted White professors to know with, “Nothing, as long as they don’t see me as black, but as a student.” Expending effort toward treating students fairly and equitably, and expressing our support and belief in their success, can simultaneously serve to make self-regulation easier and to help reinforce the notion within us that students deserve such treatment. Still, we need to be willing to commit to investing the energy when we can to create a positive interaction and relationship, and we must recognize our limits. It isn’t too much to expect faculty to put a little more effort into re-thinking their assumptions and evaluations, or to be proactive in monitoring our nonverbal behavior to be perceived in a more positive way (Feldman, 1985).

How Does the Perspective of Black Professors Differ From those of White Professors? A question about differences between the perspectives of Black and White professors allowed me to tap into student perceptions not necessarily of me personally, but of whom I represent and what I may be facing as I work to help my

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students excel. I have been pleased with students’ frankness in response to this question but a bit surprised at the level of consensus. Students convey a relatively consistent perception that their Black faculty are more personally motivated to help them succeed. As one student summarized, “Black professors will go the extra mile to see their students do well, most white professors will not.” One observation illustrates the understanding that these Black professors were once Black students, who faced heightened scrutiny: “Black professors feel like they need to impress more. You rarely see a black or minority teacher not dressed professionally with shirts tucked in or looking put together. They have to put themselves above the rest.” Although it is difficult to know whether these perceptions are a function of the particular school, the accessibility of a particular professor (Black or White) on the student’s mind, or a generally accurate perception, they give those of us who do value our students’ success pause and encourages us to communicate that desire more explicitly. Still, students also commonly look for and find allies among their White instructors; they frequently comment that they recognize who the “good guys” are on campus, and one student speculated, “If a white professor was prejudiced, I don’t think he’d work here.” Still, students returned to the notion that Black professors both demand more and give more encouragement, although not always in the form of concrete help, as they emphasize the need for independence. How do we address these perceptions in the classroom? When a student says that, “I believe black teachers take more pride in teaching black students,” how can we convey our engagement more effectively? The answer may be as straightforward as saying so. My awareness that at least one student feels that, “Black professors have attitudes but they have patience. White professors are into their job, but at the end of the day that’s all it is,” has led me to be more forthcoming with my own positive views of my students and of my belief in their potential. In many cases, however, the ways in which we convey such feedback can cause it to be misconstrued. That is, although students are generally open to professors of different races, the interpersonal dynamics do tend to become more challenging when they are interracial. Geoffrey Cohen has written extensively about interracial interactions in the classroom, and he makes some specific suggestions about how to make them more productive. In two studies, Cohen, Steele, and Ross (1999) had Black and White college students write essays about a professor and then administered one of three types of feedback to the essays. After receiving “unbuffered” criticism— critical feedback with no positive comments to cushion the blow—Black participants rated the essay reviewer as more biased and showed lower levels of task motivation than did White participants receiving the same feedback. These measures improved when a degree of positive buffer was added to the feedback, but the most positive response emerged from feedback that emphasized the need for high standards and offered a statement of the reviewer’s confidence in the

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student’s ability to meet those standards. In fact, for both racial groups, this last condition resulted also in higher identification with writing. Educators can make good use of this finding by finding ways to incorporate some positive or encouraging feedback into our criticism. We might, for example, begin by discussing some of the stronger points of a paper or exam response before moving into the weaknesses. We might comment on the work’s potential, even if the student has not yet realized that potential. Note that Cohen does not advocate simply providing more positive feedback, but rather more encouraging feedback. Rather than lowering our standards for struggling students, we should be letting them know that we have confidence in their ability to succeed. In his work, Cohen argues for the importance of establishing trust between the student and mentor (Cohen & Steele, 2002), having students write affirming essays (Cohen, Garcia, Purdie-Vaughns, Apfel, & Brzustoski, 2009), and diminishing the impact of social stigma (Cohen & Garcia, 2005). These initiatives require vigilance on the part of instructors and anyone who interacts regularly with students. As my students’ responses indicate, there is at least a perception that these interaction elements come more naturally to faculty of color, but we all share the responsibility to be proactive in examining our treatment of all of our students. Cohen and others are also addressing the issue of attribution. Attribution theory (e.g., Heider, 1958) involves how we explain the behavior of others. These explanations can be internal or external. For example, if a student does well on an exam, we could say that the student is bright (an internal attribution), or we could say that the student got lucky (an external attribution). If the student does poorly, we might say that the student is lazy (internal) or was having an off-day (external). One of the most direct ways that we can examine our treatment of our students is to consider how we are explaining their outcomes. Are we being as generous with our attributions of success (or failure) of our Black students as our White students? As one student noted, “I think if a black student messes up, it was expected. However, if a white student messes up, they were having a bad day and probably going through something.” Our natural tendency is to make more forgiving attributions for ourselves or our in-group (Tajfel & Turner, 1979), so we need to work to override this tendency and give fair attributions to all of our students, despite the effort involved.

What Is Different About the Experience at an HBCU Compared to A Predominantly White University? I included in the protocol a question about differences between HBCUs and predominately White universities in order to gain additional insight into students’ selection of their university and their views of that choice. We are all aware of the tendency to justify one’s decisions, and students are no exception when evaluating

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their choice of university. With comments such as, “I think at an HBCU, I would have a better chance as being seen more equally because almost everyone is a minority,” and, “The professors probably would not be as willing to help students,” many students expressed the belief that they would have to fight for help and for grades in what they see as a more competitive environment. They frequently cited a belief that they would have had to deal with racism at another institution. Still, students seem to be well aware that the world they will experience is increasingly diverse and that the HBCU composition is unusual. Several of them noted that they were not getting the experience of the “real world,” and a number of them mentioned that they thought their experience would have been better somewhere else, but they did not elaborate as to in what ways their educational experience would have been better. Their perceptions of the differences between these two types of institutions may indicate some of the ambivalence that students in the minority feel at any school. Even if these perceptions and assumptions are inaccurate, they may be indicative of the types of anxiety that non-White students at predominantly White universities may experience. Respondents pointed to “the whole atmosphere and the caring of African Americans succeeding,” and a sense that “You would be more detached from the school,” as an indication that they were seeking a degree of comfort at an HBCU. Students of color, whether at an HBCU or not, clearly have different expectations and experiences than other students. As one student said, “Black students see their education as a means to survive in a world where they are unwanted and white students see their education as something to do.” Another said, “Black students always feel like everything is so much harder and that there is an automatic judgment on them because they are black.” It is, therefore, important that we be aware of these differences. Some knowledge of racial identity development (e.g., Parham & Helms, 1981; Sellers, Smith, Shelton, Rowley, & Chavous, 1998) can help us to understand what students are going through in terms of their self-perceptions. At the same time, one student I talked to noted, “I don’t think white and black students differ as much as two black students.” As caring faculty, we therefore are saddled with meeting two rather disparate goals: to appreciate the different experiences between our students of different races, and to appreciate the differences within different groups of students. Concern with these goals can be daunting, but it can also be motivating and inspiring.

Conclusion How do we channel these concerns into more effective teaching and advising? As has been a recurring theme in this chapter, awareness of the situation is essential to developing an environment conducive to achieving our goals of student success. Much of this chapter has focused on the challenges—and potential rewards—of

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establishing and maintaining connections with our students so that we can better facilitate their learning. This is a challenge because all students and professors must cross certain barriers in order to reach each other. We are usually older than our students, and we often are of a different sex than our students, from a different socioeconomic background, and of course we’re generally a lot more interested in our material than our students are. We take pains to reach across those divides in order to work with our students. We frequently receive clever emails that tell us about the differences between our students and us—“Today’s freshman has never lived without the Internet” —and we often see this as a sign of their misfortune or our superior wisdom. It is easy to believe that our perspective is the better one because our perspective is so much different. In many cases, bridging gaps such as generational ones means telling our students how it will be in our class, and that they’d better just get used to it. Typically, these efforts are successful, and our students emerge with a deeper appreciation for the discipline and perhaps a better understanding of older generations. If we are effective, then we too have developed a better appreciation for the younger generations, and maybe we’ve learned to view their experience less from a smug perspective and more from one of understanding the pros and cons of the millennial generation. In response to a question about differences between HBCUs and predominately white universities, one student said, “I definitely would be worried about prejudiced teachers and students and most likely that would take a toll on how I performed at the school. It wouldn’t be totally uncomfortable but I would be more comfortable if I were around people I could relate to.” An educator, then, needs to be someone who can relate to students as much as possible. But racial differences present a special sort of divide among many of our students and us because of the salience of race in US society. Even educators who believe that race should not be a barrier to students’ educational experience and who prioritize social justice face challenges in this regard. We may unknowingly create an environment that does not ideally meet the needs of our students of color. Many faculty take the step of using codes or numbers when they grade a test, so that they will not know what student’s answers they are evaluating, thus eliminating an expectancy bias from their scores. In the classroom, however, we are unable to eliminate such biases in our interactions with students. Most of us have been tried by the vocal student whose eager responses are frequent and off-base. We might show a bit of exasperation at the irrelevant asides, wanting not to mislead the rest of the class but also not wanting to insult the student who is actually trying to answer the question. Just as our expectations for the quality of a student’s answer is likely to affect our demeanor in the class, a student’s racial category may influence our nonverbal behavior or the helpfulness of our response. Although we try to avoid allowing race or another social category to affect our degree of constructiveness in what we say, we must be vigilant to potential lapses in fairness. Just as the doctor who isn’t as

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comfortable with patients of color may give them less-than-ideal assessment and advice (Groopman, 2007), our social circle may consist primarily of financially sound, highly educated peers, and so a first-generation student of few means may also present a less comfortable interaction partner. Or in the classroom, we must realize if our skepticism about a student’s true ability comes through in the form of a somewhat dismissive response or a reluctance to provide constructive feedback. Just as faculty of my generation are generally aware that our references to Gilligan’s Island or music of the 1980s may fall on deaf (and uninterested) ears in our classes, even up-to-date references may be culturally irrelevant to minority students who are especially interested in shows that cater more to their particular demographic. Sure, most students in a classroom that is 95% White might enjoy applying psychology to Mad Men, but the other 5% may be left out of the loop, missing the application point we were trying to make, but also feeling indirectly ostracized from the group that knows something they don’t. Most educators strive for the successful development of their students. We try to coach them to learn material and to learn to think and find answers to their questions. But as we encourage critical thinking in our students, we must engage in critical thinking about ourselves and our interactions with our students. Despite our positive attitudes toward our students and our personal interest in and hopes for their success, our motives may not always come across as clearly as we’d like, especially when we have to negotiate barriers of race, class, or other social categories. Just as official barriers to success such as discriminatory laws did not change automatically or by people simply hoping that they would disappear, improvements in our own interactions with students will not occur by simply wishing it so. Rather, we must take thoughtful, self-critical actions in order to boost our students. Most of us want to be as fair and unbiased as possible in our interactions with and evaluations of our students. Nevertheless, despite our introspective and selfcritical inclinations, we are human and are not always free from the stereotypes that our society perpetuates. We simply don’t have the means to override these influences at all times. But some awareness of our vulnerability to unintentional prejudice, and a little extra effort toward making their experiences better can help us to be the faculty we truly intend to be.

References Aronson, J. (2009). Conversation #10: Stereotype threat. Podcast retrieved from http://cat. xula.edu/food/conversation–10/. Aronson, J., Fried, C. B., & Good, C. (2002). Reducing the effects of stereotype threat on African American college students by shaping theories of intelligence. Journal of Experimental Social Psychology, 38, 113–125.

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Baumeister, R. F., Muraven, M., & Tice, D. M. (2000). Ego depletion: A resource model of volition, self-regulation, and controlled processing. Social Cognition, 18, 130–150. Cohen, G. L., & Garcia, J. (2005). “I am us”: Negative stereotypes as collective threats. Journal of Personality and Social Psychology, 89, 566–582. Cohen, G. L., Garcia, J., Purdie-Vaughns, V., Apfel, N., & Brzustoski, P. (2009). Recursive processes in self-affirmation: Intervening to close the minority achievement gap. Science, 324, 400–403. Cohen, G. L., & Steele, C. M. (2002). A barrier of mistrust: How negative stereotypes affect cross-race mentoring. In J. Aronson (Ed.), Improving academic achievement: Impact of psychological factors on education. San Diego, CA: Academic Press. Cohen, G. L., Steele, C. M., & Ross, L. D. (1999). The mentor’s dilemma: Providing critical feedback across the racial divide. Personality and Social Psychology Bulletin, 25, 1302–1318. Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of Personality and Social Psychology, 56, 5–18. Feldman, R. S. (1985). Nonverbal behavior, race, and the classroom teacher. Theory into Practice, 24, 45–49. Gilbert, D. T., Pelham, B. W., & Krull, D. S. (1988). On cognitive busyness: When person perceivers meet persons perceived. Journal of Personality and Social Psychology, 54, 733–740. Good, C., Aronson, J., & Inzlicht, M. (2003). Improving adolescents’ standardized test performance: An intervention to reduce the effects of stereotype threat. Journal of Applied Developmental Psychology, 24, 645–662. Groopman, J. (2007). How doctors think. Boston, MA: Houghton-Mifflin. Hammer, E. D. (1996, July). Low-prejudiced perceivers need cognitive capacity to moderate initial stereotypic dispositional attributions. Poster presented at the Eighth American Psychological Society Annual Convention. Washington, DC. Heider, F. (1958). The psychology of intergroup relations. New York: Wiley. Marx, D. M., & Goff, P. A. (2005). Clearing the air: The effect of experimenter race on target’s test performance and subjective experience. British Journal of Social Psychology, 44, 645–657. Mason, M. F., Cloutier, J., & Macrae, C. N. (2006). On construing others: Category and stereotype activation from facial cues. Social Cognition, 24, 540–562. McGlone, M. S., & Aronson, J. (2006). Stereotype threat, identity salience, and spatial reasoning. Journal of Applied Developmental Psychology, 27, 486–493. Monteith, M. J., Sherman, J. W., & Devine, P. G. (1998). Suppression as a stereotype control strategy. Personality and Social Psychology Review, 2, 63–82. Nguyen, H. D., & Ryan, A. M. (2008). Does stereotype threat affect test performance of minorities and women? A meta-analysis of experimental evidence. Journal of Applied Psychology, 93, 1314–1334. Parham, T. A., & Helms, J. E. (1981). The influence of Black students’ racial identity attitudes on preference for counselor’s race. Journal of Counseling Psychology, 28, 250–257.

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Richeson, J. A., & Shelton, J. N. (2003). When prejudice does not pay: Effects of interracial contact on executive function. Psychological Science, 14, 287–290. Sellers, R. M., Smith, M. A., Shelton, J. N., Rowley, S. A. J., & Chavous, T. M. (1998). Multidimensional model of racial identity: A reconceptualization of African American racial identity. Personality and Social Psychology Review, 2, 18–39. Shelton, J. N., & Richeson, J. A. (2006). Interracial interactions: A relational approach. In M. P. Zanna (Ed.), Advances in experimental social psychology, 38. San Diego, CA: Elsevier Academic Press. Shih, M., Pittinsky, T. L., & Ambady, N. (1999). Stereotype susceptibility: Identity salience and shifts in quantitative performance. Psychological Science, 10, 80–83. Steele, C. M. (1997). A threat in the air: How stereotypes shape intellectual identity and performance. American Psychologist, 52, 613–629. Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African Americans. Journal of Personality and Social Psychology, 69, 797–811. Sweet, L. J., Szaniszlo, M., Crimaldi, L., Van Sack, J., & Dwinell, J. (2009, July 23). Officer in Henry Gates flap tried to save Reggie Lewis. Boston Herald. Retrieved from http:// www.bostonherald.com/news/regional/view/20090722cop_who_arrested_henry_ gates_im_not_apologizing/srvc¼home&position¼0. Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In W. G. Austin & S. Worchel (Eds.), The social psychology of intergroup relations. Monterey, CA: Brooks/Cole. Word, C. O., Zanna, M. P., & Cooper, J. (1974). The nonverbal mediation of self-fulfilling prophecies in interracial interaction. Journal of Experimental Social Psychology, 10, 109–120.

5 Teaching in Ways that Support Students’ Autonomy Johnmarshall Reeve

This chapter focuses on what students need from their teachers if they are to engage themselves freely and productively in learning activities and thereby learn, develop skills, and position themselves to become the sort of person they strive to become. For these student outcomes to happen, students need many instructional supports, such as interesting lessons, clear goals, timely scaffolding, feedback about their progress, and so forth. From a motivational perspective, however, what students need most from their teachers is support for their autonomy. Recognizing this, the purpose of the chapter is to offer teachers empirically-validated recommendations on how to support students’ autonomy during instruction. The theoretical framework that makes these teaching recommendations possible is self-determination theory (SDT). SDT is an approach to student motivation that uses empirical methods to investigate the role that students’ inner motivational resources play in explaining their classroom engagement and effective functioning (Ryan & Deci, 2000, 2002). Some key inner motivational resources that teachers can assume all students possess are a psychological need for autonomy, intrinsic motivation, and personal preferences that reflect their selfendorsed goals and values. A key observation of the theory is that students are sometimes agentic, engaged, and responsible but are other times passive, listless, and irresponsible, and it is the extent to which the social context energizes and catalyzes (vs. frustrates and undermines) students’ motivation that explains an important part of these different ways of approaching a learning activity. What this means in a practical sense is that teachers can assume that students walk into the classroom possessing reliable and engagement-fostering motivational

Empirical Research in Teaching and Learning: Contributions from Social Psychology Edited by Debra Mashek & Elizabeth Yost Hammer © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33719-8

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resources to the point that the teacher’s role becomes the offering of instructional opportunities that support and nurture (rather than neglect and thwart) these motivational resources.

Part 1. Theoretical Foundation: Understanding Students’ Motivation and Teachers’ Motivating Styles Research guided by SDT leads to the conclusion that student functioning is most positive when students experience high autonomy during learning and when teachers support their autonomy during instruction (Reeve, 2006a, 2009). Because this is so, it will be helpful to introduce both of these concepts—student autonomy and a teacher’s motivating style—before offering recommendations on how teachers might support students’ autonomy.

Student Autonomy Autonomy is the inner endorsement of one’s actions—the sense that one’s actions emanate from oneself and are one’s own (Deci & Ryan, 1985). When autonomous, students’ goals and actions flow out of an internal perceived locus of causality, a sense of volition, and an experience of choice (Reeve, Nix, & Hamm, 2003). An internal locus is the perception that the causal source of one’s motivated action is oneself and that one’s ensuing action is congruent with and regulated by one’s self. Its opposite is an external locus of causality, which is the perception that the causal source of one’s motivated action is some force outside the self, such as an attractive incentive or a pressing deadline. Volition is an experience of feeling free, and it involves an unpressured willingness to engage in an activity. Its opposite is a sense of pressure in which one “has to” do something (e.g., “Though I don’t really want to, I have to study tonight.”). Perceived choice over one’s actions reflects an ongoing decision-making flexibility to choose what do to, how to do it, whether or not to change one’s course of action, or to stop altogether and do something else. Its opposite is a sense of obligation or inflexible assignment. The psychological construct that integrates these three subjective experiences into a single, coherent motivational experience is autonomy. While it is important in its own right, the psychological need for autonomy further underlies students’ intrinsic motivation and self-endorsed goals and values. Intrinsic motivation is engaging an activity for the inherent satisfaction of the activity itself or, more formally, it is the inherent propensity to engage one’s interests and to exercise one’s capacities and, in doing so, to explore, to seek out and master optimal challenges, and to learn (Deci & Ryan, 1985). An example of how a student might express intrinsic motivation during a learning activity is to show a high and enduring level of interest, to seek out and find pleasure in optimal

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challenge, and to say things such as, “This is fun.” Self-endorsed goals and values typically reflect students’ autonomous extrinsic motivation. Extrinsic motivation becomes autonomous when students volitionally internalize into the self-system a socially-prescribed way of thinking or behaving (e.g., a goal of graduating high school, a value for practicing a musical instrument). An example of how a student might express a self-endorsed goal or value during instruction is through a high and enduring level of commitment, a valuing of what he or she is doing, as well as uttering sentiment such as, “Even though this is not fun, it is important to me.” What is common among students’ autonomy, intrinsic motivation, and selfendorsed goals and values is that action flows out of students’ internal locus of causality, an experience of feeling free, and a sense of perceived choice in whether or not to engage in the activity.

Teacher’s Motivating Style Motivating style represents the quality or ambience of a teacher’s interpersonal style toward students during instructional episodes in which the teacher tries to encourage students to initiate or regulate engagement in a learning activity (Reeve, 2009). For instance, a teacher might try to encourage a student to read a book, follow a rule, or improve performance, and motivating style captures the quality of the teacher’s sentiment (the tone of interaction) and behavior (what the teacher says and does) while trying to spark, encourage, and sustain such initiative and persistence. It can be conceptualized along a bi-polar continuum that ranges from a highly controlling style on one end of the continuum through a somewhat controlling or somewhat autonomy-supportive style to a highly autonomysupportive style on the other end of the continuum (Deci, Schwartz, Sheinman, & Ryan, 1981). A teacher’s motivating style toward students is an important educational construct (and a topic worthy of inclusion in the present book) because students of autonomy-supportive teachers display markedly more positive classroom functioning and educational outcomes than do students of controlling teachers (Deci & Ryan, 1985; Reeve, 2009; Reeve & Jang, 2006; Ryan & Deci, 2000). Autonomy support is the interpersonal sentiment and behavior teachers provide during instruction to identify, nurture, and develop students’ inner motivational resources (Assor, Kaplan, & Roth, 2002; Reeve, 2006a; Reeve, Deci, & Ryan, 2004). In practice, autonomy support is what teachers say and do to first identify, then nurture, and eventually develop and strengthen students’ inner motivational resources. That is, using a student’s self-endorsed (internalized) value as an example, an autonomy-supportive teacher would initially identify the extent to which the student found the learning activity an important thing to do, asking “Does this seem like a worthwhile thing to do?” The autonomy-supportive teacher would then nurture such a motivational resource by structuring students’ time with the learning activity to allow time for students to do what is particularly

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important to them. In addition, an autonomy-supportive teacher would create opportunities to develop or strengthen students’ lesson-relevant values by explaining how the lesson might be a truly useful thing to do. The opposite of autonomy support is a controlling motivating style, which is the interpersonal sentiment and behavior teachers provide during instruction to pressure students to think, feel, or behave in a specific (typically teacher-defined) way (Assor, Kaplan, Kanat-Maymon, & Roth, 2005; Reeve, 2009; Reeve, Deci et al., 2004). In practice, controlling teachers neglect or discount engagementfostering aspects of students’ inner motivational resources and, instead, try to initiate and regulate students’ classroom engagement by (1) telling or prescribing what students are to do and (2) applying subtle or not-so-subtle pressure until students forego their own preferences to adopt the teacher’s prescribed course of action. The controlling teacher would recommend an action (e.g., “revise your paper,” “follow the rule,” “participate more in the group discussion”) and add a twist of pressure to raise the likelihood that the student would indeed enact the prescribed action (e.g., offer an incentive, invoke a deadline, use pressuring language such as “pay attention,” or “make sure the project is complete, or else it won’t count towards class credit”).

Part 2. Application to Classroom Practice: How to Become More Autonomy Supportive In practice, autonomy-supportive acts of instruction include nurturing inner motivational resources, providing rationales, relying on noncontrolling language, displaying patience to allow time for learning, and accepting students’ expressions of negative affect (Reeve, 2009). These acts of instruction represent empiricallyvalidated instructional behaviors that have been shown to nurture and support students’ experience of autonomy and engagement (Reeve & Jang, 2006; Reeve, Jang, Carrell, Barch, & Jeon, 2004). As such, each of these five acts of instruction can function as a recommendation as to how teachers might teach in ways that support students’ autonomy. Before providing such specifics, however, it might be helpful to pause here for a moment to make four preliminary, but crucially important, points—namely, that students benefit from receiving autonomy support, that teachers benefit from giving autonomy support, that teachers can learn how to become more autonomy supportive, and teachers need to ready themselves to become more autonomy supportive.

Students Benefit from Receiving Autonomy Support Collectively, the autonomy-supportive acts of instruction to be summarized in Figures 5.1 through 5.5 provide students with an interpersonal relationship that affords them with opportunities to experience learning activities within a

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motivational climate of personal autonomy. As mentioned earlier, a wealth of empirical study shows that students benefit when teachers support their autonomy (and they suffer when teachers control their behavior). The important point here is that this conclusion has been shown to be true whether the benefit refers to students’ subsequent motivation (e.g., Reeve et al., 2003), engagement (e.g., Skinner & Belmont, 1993), healthy development (e.g., Deci et al., 1981), learning (e.g., Vansteenkiste, Simons, Lens, Sheldon, & Deci, 2004), performance (e.g., Black & Deci, 2000), or psychological well-being (e.g., Nix, Ryan, Manly, & Deci, 1999). Further, this conclusion applies to students of all grade levels, including students in undergraduate (Black & Deci, 2000) and graduate (Sheldon & Krieger, 2004) settings. This conclusion also generalizes across a wide range of student characteristics (e.g., gender, grade, special needs, nationality, and cultural value orientation; Chirkov & Ryan, 2001; Jang, Reeve, Ryan, & Kim, 2009).

Teachers Benefit from Giving Autonomy Support At first blush, autonomy support seems to be all about giving to others. It certainly benefits students, but it is a fair question to ask if teachers benefit from autonomy giving. Recent research shows that givers of autonomy support do experience meaningful benefits, including an increased sense of personal accomplishment and a decreased sense of emotional exhaustion from their teaching, compared to their relatively controlling classroom counterparts (Roth, Assor, Kanat-Maymon, & Kaplan, 2007). Giving autonomy support also, over time, leads to greater relationship satisfaction (Deci, La Guardia, Moller, Scheiner, & Ryan, 2006). Thus, like their students, teachers too benefit from the autonomy support they provide.

Teachers Can Learn How to Become More Autonomy Supportive Intervention-based research in which teachers participate in informational and mentoring sessions on how to support students’ autonomy shows that teachers can indeed learn to become more autonomy supportive toward students (Su & Reeve, in press). Further, this positive training effect has been shown to occur for inexperienced preservice teachers (Reeve, 1998) as well as for highly experienced teachers, including those teaching middle-school (deCharms, 1976), high-school (Reeve, Jang et al., 2004), and college (Williams & Deci, 1996).

What Teachers Need to do to Ready Themselves to Become More Autonomy Supportive Before teachers can become highly autonomy supportive, it seems that they need to approach instruction and student motivation in new ways. The three conditions that make any approach to instruction autonomy supportive are the following: (1) become aware of the students’ perspective during learning activities;

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(2) welcome students’ thoughts, feelings, and behaviors into the flow of instruction; and (3) support students’ motivational development and capacity for autonomous self-regulation (Reeve, 2009). By taking and integrating the students’ perspective into the day’s lesson plan, teachers become both more willing and more able to create classroom conditions in which students’ autonomous motivations guide their classroom activity. By welcoming students’ ways of thinking, feeling, and behaving into the flow of instruction, teachers acknowledge and appreciate the motivational potential inherent within students’ thoughts, emotions, and behavioral intentions. By acknowledging students’ ongoing developmental capacity for autonomous selfregulation, teacher–student interactions revolve around not only supporting students’ motivation for the day but supporting students’ longer-term (developmental) capacity to generate and regulate academic motivation of their own.

Nurture Inner Motivational Resources (see Figure 5.1) Part 1 of this chapter identified four inner motivational resources that teachers can expect all students to possess—the psychological need for autonomy, intrinsic motivation, self-endorsed (intrinsic) goals, and self-endorsed (internalized) values. In addition, students can be expected to possess additional inner motivational resources, including a sense of curiosity, a preference for optimal challenge, and a few others as well (see Reeve, Deci et al., 2004). Nurturing inner motivational resources means building instruction around opportunities to have students’ classroom engagement initiated and regulated by these inner resources. That is, the reason why students engage in the lesson is because it is satisfying (fun),

Nurturing Inner Motivational Resources (Autonomy-Supportive Instructional Behavior number 1) When Teachers Need It Most: * When introducing a learning activity or making a transition to a new activity. * When seeking student initiative and initial task engagement.

Autonomy-Supportive Instruction:

Controlling Instruction:

Encourage Student Initiative by Involving Inner Motivational Resources, such as:

Encourage Student Initiative by Relying on Environmental Sources of Motivation, such as:

Psychological Need for Autonomy

Directives, Commands

Intrinsic Motivation

Compliance Requests, Assignments

Self-Endorsed Goals and Values

Incentives

Preference for Optimal Challenge

Rewards (External Contingencies)

Sense of Curiosity Why It Is So Important:

* Allows students to feel like origins, rather than pawns, during learning activities. * Aligns what students do with an authentic sense of wanting to do it.

Figure 5.1 Nurturing inner motivational resources.

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meaningful (important), goal-relevant, curiosity-piquing, challenge inviting, etc., and not because they have to (e.g., to obey a directive, to fulfill a request, to earn an incentive such as extra credit points). It is a particularly useful approach to motivation when introducing a learning activity and seeking students’ initial engagement. For instance, autonomy-supportive instruction might begin a lesson with (1) a curiosity-inducing question (e.g., a science teacher might ask, “Which came first—dinosaurs or grass?” Such as question might pique curiosity because, surprisingly, dinosaurs were extinct long before the first grass ever grew. Hence, curiosity around “Where did grass come from?” and “Where did the soil come from?” might add a sense of curiosity to a lesson on plants or the environment), (2) the provision of an optimal challenge (e.g., “Here is a question/problem for you; let’s see if you can figure it out. . .”), or (3) an invitation to use the lesson as an opportunity to make progress on an intrinsic goal (e.g., an English teacher might begin the day’s lesson saying, “Today we are going to read a passage by the writer Philip Roth. As you read, notice how good the writing is. Ask yourself what makes this such good writing, and use your answer to discover how to become a better writer yourself.”). The idea is that students’ naturally want to do what is curiosityarousing, optimally challenging, and relevant to their personal goals. In contrast, controlling instruction ignores or by-passes opportunities to involve and nurture students’ inner resources and, instead, relies on artificial or pressuring sources of motivation to manufacture student engagement.

Provide Explanatory Rationales (see Figure 5.2) A rationale is a verbal explanation of why putting forth effort during the activity might be a useful thing to do (Reeve, Jang, Hardre, & Omura, 2002). Providing

Providing Explanatory Rationales (Autonomy-Supportive Instructional Behavior number 2)

When Teachers Need It Most:

* When asking students to engage in uninteresting (but important) lessons. * When asking students to engage in requested activities, rules, or procedures.

Autonomy-Supportive Instruction:

Controlling Instruction:

Provide Explanatory Rationales by Communicating or Revealing the Endeavor’s Underlying:

Neglect to Communicate Why an Activity Is Worth Doing or a Rule is Worth Following. Hence, Students Experience:

Importance Personal Usefulness to the Student

Activities as Meaningless Busy Work

Value, Especially Hidden Value

Rules as Arbitrary or Counter to Their Wishes

Meaning, Personal Significance to the Student

Directives, without Explanation

Why It Is So Important:

* Promotes internalization. * Students need help to transform what is not worth doing into what is worth doing.

Figure 5.2 Providing explanatory rationales.

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rationales means communicating to students the usefulness of an activity or course of action, a personal utility that is sometimes (and perhaps often) hidden from students’ awareness or personal experience. That is, as students face a learning activity that they do not really want to do (e.g., “Is it really necessary that we do this?”), teachers can help support students’ otherwise fragile motivation by providing explanatory rationales, such as “The reason I’m asking you to do this is because. . .” It is a particularly useful approach to motivation when asking students to engage in an activity, follow a rule, or enact a procedure that is an uninteresting thing to do (from their point of view). The idea is that honest, valid, and satisfying rationales afford students an opportunity to internalize the value of what others (e.g., teacher, community) value and request they do. With internalization, students can bring originally extrinsic sources of motivation (“We have a rule that you need to complete this paperwork each month.”) into the self-system as a personally accepted or endorsed source of motivation (“I want to complete the paperwork so I will be eligible for my health care benefits.”). When students do not understand why the teacher is making a request of them, they often view the request as arbitrary, imposed, or simply meaningless busywork. As a rule of thumb, controlling teachers make requests, while autonomy supportive teachers make requests and provide supportive rationale to justify the merit of the request.

Rely on Noncontrolling Language (see Figure 5.3) Noncontrolling language is communication that is nonevaluative, flexible, and informational. Noncontrolling means avoiding messages that communicate or create pressure (“you should. . . you have to. . . you must. . .”) and external

Using Noncontrolling, Informational Language (Autonomy-Supportive Instructional Behavior number 3) When Teachers Need It Most:

* When communicating requirements, responsibilities, and feedback. * When addressing motivational and behavioral problems.

Autonomy-Supportive Instruction:

Controlling Instruction:

Communicate Classroom Requirements and Address Problems through Messages that Are:

Communicate Classroom Requirements and Address Problems through Messages that Are:

Nonevaluative

Evaluative

Flexible

Rigid

Informational

Pressuring

Why It Is So Important:

* Maintains a positive student-teacher relationship * Helps students diagnose their motivational, behavioral, and performance problems while maintaining their personal responsibility for those problems.

Figure 5.3 Using noncontrolling, informational language.

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evaluation. Informational means providing insight that helps the student diagnose, understand, and solve the problem being discussed (e.g., poor performance, listlessness). For instance, autonomy-supportive instruction would begin a discussion of students’ poor performance or woeful class attendance by communicating a problem the teacher has noticed and asking the student about it (e.g., “I’ve noticed that you made a surprisingly low score on the test. Do you know why that might be?”). Relying on noncontrolling and informational language is a particularly useful approach to motivation when communicating requirements and responsibilities, when offering feedback, when discussing strategies, and when addressing the motivational and behavioral problems that might arise during instruction. The idea is to address a problem while preserving students’ sense of ownership and responsibility (i.e., an internal locus of causality) for regulating their own behavior and for solving their own problems. In contrast, controlling instruction verbally pushes and pressures the student toward a teacher-specified solution, product, or desired behavior without enlisting the students’ problemsolving effort (e.g., “you must improve your grades.”, “your attendance is not acceptable; I am penalizing you 10 points until your attendance improves.”).

Display Patience to Allow Time for Self-Paced Learning to Occur (see Figure 5.4) Patience is the calmness a teacher shows as students struggle to understand or adjust their behavior. Straining circumstances such as time constraints and highstakes testing make it easy to understand why teachers are not patient, but the

Displaying Patience To Allow Time For Self-paced Learning To Occur (Autonomy-Supportive Instructional Behavior number 4) When Teachers Need It Most:

* When students try to develop skill or learn something unfamiliar or complex. * When learning activities require experimentation, reflection, and revision.

Autonomy-Supportive Instruction:

Controlling Instruction:

Display Patience While Students:

Impatiently Interrupt Student Learning to:

Explore and Manipulate the Learning Materials

Intrude on the Students’ Workspace

Make Plans, Set Goals

Utter a Solution or Answer

Formulate and Test Hypotheses

Show a Solution or Answer

Monitor and Revise their Work

Say, “Here, let me do that for you.”

Alter their Problem-Solving Strategies

Grab Away or “Hog” the Learning Materials

Why It Is So Important:

* Learning—accommodation, conceptual change, deep information processing— takes time, even if teachers feel that they don’t have that time to give.

Figure 5.4 Displaying patience to allow time for self-paced learning to occur.

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reason to be patient (motivationally speaking) comes from a deep valuing for the student’s internal locus of causality (personal causation) and an understanding that learning processes such as conceptual change and deep processing take time. Displaying patience means that students need both time and space to explore and manipulate learning materials, formulate and test hypotheses, make plans and set goals, monitor and revise their work, and alter their problem-solving strategies. It is a particularly useful approach to motivation when students involve themselves in learning activities that are unfamiliar, complex, or involve the development and refinement of a skill. For instance, autonomy-supportive instruction would postpone advice until understanding the student’s goals and perspective and provide scaffolding (e.g., hints when students seem stuck) when it is needed and invited. In contrast, controlling instruction impatiently rushes in to show or tell students the answer or solution (i.e., “Here, let me show you how to do it.”), thereby by-passing the learning opportunity itself.

Acknowledge and Accept Students’ Expressions of Negative Affect (see Figure 5.5) Negative affect involves students’ complaints, negative emotion and feelings, resistance, counter-protests, and “bad attitude.” Acknowledging and accepting such negative affectivity means taking to heart and even welcoming these expressions as potentially valid reactions to imposed rules, assignments, requests, expectations, demands or structures. It is a particularly useful approach to motivation during conflicts as to what teachers want students to do (e.g., read

Acknowledging And Accepting Expressions Of Resistance And Negative Feelings (Autonomy-Supportive Instructional Behavior number 5) When Teachers Need It Most: * When students try to develop skill or learn something unfamiliar or complex. * When students’ preferences are at odds with the teacher’s requests and requirements.

Autonomy-Supportive Instruction:

Controlling Instruction:

Acknowledge and Accept Students’ Expressions of Resistance and Negative Feelings:

Counter and Try to Change Students’ Expressions of Resistance and Negative Feelings:

Acknowledge

Counter

Accept

Argue Against

Welcome

Try to Change

Expressions of Resistance and Negative Feelings as Potentially Valid Reactions to Imposed Demands, Constraints, and Structures.

Such “Bad Attitude” Is Unacceptable and Needs to be Changed, Fixed, or Reversed into Something More Acceptable to the Teacher.

Why It Is So Important:

* To appreciate the students’ perspective, and to give voice to that perspective. * Creates an opportunity to restructure an otherwise unappealing lesson.

Figure 5.5 Acknowledging and accepting expressions of negative affect.

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a book, revise a paper) and what students want students to do (e.g., something different, something less demanding). For instance, autonomy-supportive instruction would acknowledge a conflict (e.g., “I see that you all are not very interested in today’s lesson.”), accept students’ expressions of disinterest as potentially valid (e.g., “Yes, we have practiced this same skill many times before.”), and welcome students’ input and suggestions on how to resolve the conflict (e.g., “Let’s see; what might we do differently—any suggestions?”). The idea is that students’ negative feelings, if unaddressed, will interfere with their engagement and learning. Soothing negative feelings therefore seems a prerequisite to motivationally readying students to accept the forthcoming lesson and to learn from it. In contrast, controlling instruction does not see students’ resistance as valid (“You’re immature; you’re irresponsible.”) and, hence, controlling teachers counter or otherwise try to change that resistance and those negative feelings into something more acceptable to the teacher (e.g., “Quit your complaining; now get to work and do what you are supposed to do.”).

Epilogue. Appreciating Carl Rogers: Solving a Key Dilemma of Teaching Carl Rogers contributed immensely to psychological thinking on personality and motivation. In 1952 he addressed a group of college teachers in which he communicated some rather startling personal reflections on teaching, including “I have lost interest in being a teacher”, and “When I try to teach, as I sometimes do, I am appalled by the results, which seem a little more than inconsequential, because sometimes the teaching appears to succeed. When this happens I find that the results are damaging. It seems to cause the individual to distrust his own experience, and to stifle significant learning. Hence, I have come to feel that the outcomes of teaching are either unimportant or hurtful” (see Rogers, 1969, p. 152). Rogers’ key lamentation was that his well-intended efforts to help others acquire knowledge, develop skill, and realize their potential too often had the unintended side-effect of interfering with their internal locus of causality, internal locus of evaluation, and sense of personal agency as a learner. Part of the problem, and perhaps most of it, is that teachers have two sets of concerns—one for their students but another for the priorities and needs of the school and its curriculum, as teachers worry about their responsibilities to cover the course content, meet state standards, raise students’ achievement scores, and answer to concerns and criticisms voiced by various constituents such as parents and school administrators. In this context, a concern over student motivation might at times take a back seat to the teacher’s daily pressures and needs (e.g., “I would like to have an interesting group discussion today, but I have to make sure we cover all this material by the end of the week.”)

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Teachers generally do a good job of promoting and honoring the needs and preferences of the school and its curriculum. The way teachers promote and honor the needs of the school is, generally speaking, to provide students with a highly structured classroom experience in which teachers communicate to students the teacher’s (or school’s) expectations, standards, expected outcomes, requirements, priorities, goals, plans, and schedule of events (Reeve, 2006b). The problem with structure is that it can, potentially, over-script learning and take a sense of personal responsibility—or what Richard deCharms’ (1976) called a sense of “personal causation”—away from students. But it’s opposite— permissiveness—is no better, potentially worse even (Hickey, 1997). What Rogers’ needed was a viable and workable third option. Our program of research has been about finding that third option, articulating how it is practiced in the classroom, and validating its educational and developmental benefits. That third option is an autonomy-supportive approach to teaching or, more precisely, it is to provide a structured classroom in an autonomy-supportive way (see Jang, Reeve, & Deci, 2010). The classroom challenge autonomy-supportive teachers typically face, motivationally-speaking, is how to introduce students to school-valued expected outcomes, goals, communications, rules, rewards, feedback, and other structure-enhancing elements in autonomy-supportive, rather than in controlling, ways. Autonomy support takes the perspective of the student, and it values that perspective. It does not, however, downplay the importance and necessity of taking, valuing, and acting on the teacher’s (or school’s) perspective during instruction. A teacher’s plans, priorities, and goals (i.e., perspective) can be expressed in autonomy-supportive ways. Further, when trained raters observe teachers they consistently find that autonomy-supportive teachers are more likely, not less likely, to offer a highly structured learning environment (Jang et al., 2010; Sierens, Vansteenkiste, Goossens, Soenens, & Dochy, 2009). That is, the same teachers who clearly communicate their expectations, set high standards, introduce classroom goals, make instructional priorities salient, show strong guidance during learning activities, and provide a wealth of feedback are the ones who are more likely (not less likely) to nurture students’ inner motivational resources, provide explanatory rationales, rely on noncontrolling language, display patience, and acknowledge and accept expressions of negative affect. This means, in practice, that teachers do not need to totally overhaul what they do in the classroom to become more autonomy supportive. Rather, what it means is that teachers need to adapt what they already do (provide structure) so that they support autonomy rather than control behavior throughout the implementation of the lesson plan and curriculum more generally. The point of the five autonomy-supportive acts of instruction summarized in Figures 5.1 through 5.5 is to supply the information teachers need to make that adaptation.

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References Assor, A., Kaplan, H., Kanat-Maymon, Y., & Roth, G. (2005). Directly controlling teacher behaviors as predictors of poor motivation and engagement in girls and boys: The role of anger and anxiety. Learning and Instruction, 15, 397–413. Assor, A., Kaplan, H., & Roth, G. (2002). Choice is good, but relevance is excellent: Autonomy-enhancing and suppressing teaching behaviors predicting students’ engagement in schoolwork. British Journal of Educational Psychology, 27, 261–278. Black, A. E., & Deci, E. L. (2000). The effects of instructors’ autonomy support and students’ autonomous motivation on learning organic chemistry: A self-determination theory perspective. Science Education, 84, 740–756. Chirkov, V., & Ryan, R. M. (2001). Parent and teacher autonomy-support in Russian and U.S. adolescents: Common effects on well-being and academic motivation. Journal of Cross Cultural Psychology, 32, 618–635. deCharms, R. (1976). Enhancing motivation: Change in the classroom. New York: Irvington. Deci, E. L., La Guardia, J. G., Moller, A. C., Scheiner, M. J., & Ryan, R. M. (2006). On the benefits of giving as well as receiving autonomy support: Mutuality in close friendships. Personality and Social Psychology Bulletin, 32, 313–327. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum. Deci, E. L., Schwartz, A., Sheinman, L., & Ryan, R. M. (1981). An instrument to assess adult’s orientations toward control versus autonomy in children: Reflections on intrinsic motivation and perceived competence. Journal of Educational Psychology, 73, 642–650. Hickey, D. T. (1997). Motivation and contemporary socio-constructivist instructional perspectives. Educational Psychologist, 32, 175–193. Jang, H., Reeve, J., & Deci, E. L.(2010). Engaging students in learning activities: It’s not autonomy support or structure but autonomy support and structure. Journal of Educational Psychology, 102, 588–600. Jang, H., Reeve, J., Ryan, R. M., & Kim, A. (2009). Can self-determination theory explain what underlies the productive, satisfying learning experiences of collectivistically-oriented Korean students? Journal of Educational Psychology, 101, 644–661. Nix, G. A., Ryan, R. M., Manly, J. B., & Deci, E. L. (1999). Revitalization through selfregulation: The effects of autonomous and controlled motivation on happiness and vitality. Journal of Experimental Social Psychology, 35, 266–284. Reeve, J. (1998). Autonomy support as an interpersonal motivating style: Is it teachable? Contemporary Educational Psychology, 23, 312–330. Reeve, J. (2006a). Teachers as facilitators: What autonomy-supportive teachers do and why their students benefit. Elementary School Journal, 106, 225–236. Reeve, J. (2006b). Extrinsic rewards and inner motivation. In C. Weinstein & T. L. Good (Eds.), Handbook of classroom management: Research, practice, and contemporary issues (pp. 645–664). Hillsdale, NJ: Lawrence Erlbaum.

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Reeve, J. (2009). Why teachers adopt a controlling motivating style toward students and how they can become more autonomy supportive. Educational Psychologist, 44, 159–175. Reeve, J., Deci, E. L., & Ryan, R. M. (2004). Self-determination theory: A dialectical framework for understanding the sociocultural influences on student motivation. In D. McInerney & S. Van Etten (Eds.), Research on sociocultural influences on motivation and learning: Big theories revisited (Vol. 4, pp. 31–59). Greenwich, CT: Information Age Press. Reeve, J., & Jang, H. (2006). What teachers say and do to support students’ autonomy during a learning activity. Journal of Educational Psychology, 98, 209–218. Reeve, J., Jang, H., Carrell, D., Barch, J., & Jeon, S. (2004). Enhancing high school students’ engagement by increasing their teachers’ autonomy support. Motivation and Emotion, 28, 147–169. Reeve, J., Jang, H., Hardre, P., & Omura, M. (2002). Providing a rationale in an autonomysupportive way as a strategy to motivate others during an uninteresting activity. Motivation and Emotion, 26, 183–207. Reeve, J., Nix, G., & Hamm, D. (2003). Testing models of the experience of selfdetermination in intrinsic motivation and the conundrum of choice. Journal of Educational Psychology, 95, 375–392. Rogers, C. R. (1969). Freedom to learn. Columbus, OH: Merrill. Roth, G., Assor, A., Kanat-Maymon, Y., & Kaplan, H. (2007). Autonomous motivation for teaching: How self-determined teaching may lead to self-determined learning. Journal of Educational Psychology, 99, 761–774. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68–78. Ryan, R. M., & Deci, E. L. (2002). An overview of self-determination theory. In E. L. Deci & R. M. Ryan (Eds.), Handbook of self-determination research (pp. 3–33). Rochester, NY: University of Rochester Press. Sheldon, K. M., & Krieger, L. (2004). Does law school undermine law students? Examining change in goals, values, and well-being. Behavioral Sciences and the Law, 22, 261–286. Sierens, E., Vansteenkiste, M., Goossens, L., Soenens, B., & Dochy, F. (2009). The synergistic relationship of perceived autonomy support and structure in the prediction of selfregulated learning. British Journal of Educational Psychology, 79, 57–68. Skinner, E. A., & Belmont, M. J. (1993). Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year. Journal of Educational Psychology, 85, 571–581. Su, Y., & Reeve, J. (in press). A meta-analysis of the effectiveness of intervention programs designed to support autonomy. Educational Psychology Review. Vansteenkiste, M., Simons, J., Lens, W., Sheldon, K. M., & Deci, E. L. (2004). Motivating learning, performance, and persistence: The synergistic role of intrinsic goals and autonomy support. Journal of Personality and Social Psychology, 87, 246–260. Williams, G. C., & Deci, E. L. (1996). Internalization of biopsychosocial values by medical students: A test of self-determination theory. Journal of Personality and Social Psychology, 70, 115–126.

6 Achievement is an Attitude The Importance of Help-Seeking Attitudes when Predicting Academic Achievement Jessica Clevering, Shelley DeFord, Tasia Yamamura, and Debra Mashek

When students encounter problems, they do not have to struggle alone—they can seek help from peers, instructors, tutors, and resource centers. Not all students do this, however, and much research has been conducted on student characteristics that predict such help seeking behaviors (Karabenick & Knapp, 1991; Nadler, 1983; Ryan, Patrick, & Shim, 2005; Ryan & Pintrich, 2006; Ryan, Pintrich, & Midgley, 2001). Although several studies have evaluated the relationship between academic help seeking and academic performance, most have looked at help seeking in specific courses; few have studied help seeking across academic subjects. Fewer still have followed the relationship between the predictors of help seeking (such as attitudes toward help seeking), help-seeking behaviors, and academic performance throughout an academic year. Understanding these longitudinal relationships is important for understanding help-seeking attitudes and academic achievement in real-world settings. Scholars in the field of educational psychology have tried to unravel the reasons why some students seek help while others do not. Perhaps the most studied construct in predicting help-seeking behavior is student attitudes toward help seeking. Attitudes toward help seeking have been theorized to correlate with actual help-seeking behaviors (Ryan & Pintrich, 1997): students with more open attitudes toward help seeking will presumably seek help more. Another way of

Empirical Research in Teaching and Learning: Contributions from Social Psychology Edited by Debra Mashek & Elizabeth Yost Hammer © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33719-8

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saying this is that the more avoidant a student’s attitude toward help seeking, the less the student will actually seek help. Data support the existence of this relationship. For instance, Butler (1998) found that among 10- to 12-year-old math students, those who had an avoidant attitude toward help seeking asked for help less than other students when working on math problems. The avoidant attitude in this case was due to students’ concern over their math ability. A component of an avoidant attitude toward help seeking may be concern about perceived academic ability. Some students dislike the idea of asking for help because they believe that asking for help would make other people see them as unintelligent. For example, in a study of 612 college students, an avoidant attitude toward help seeking was measured using survey items such as “I would feel uneasy about what people would think if they found out I needed help in order to succeed academically.” These items were created to measure how much students viewed help-seeking behaviors as a threat to their self-esteem. When asked about their behavioral intentions, the more students agreed with such statements the less they intended to engage in both formal and informal help-seeking behaviors (Karabenick & Knapp, 1991). Although a good deal of research suggests attitudes toward help seeking predict help-seeking behavior, less is known about the actual academic benefits of such help seeking. Presumably, students seek academic help believing that doing so will result in academic benefit. Among the studies examining the relationship between help-seeking behavior and academic achievement, many look at help seeking in specific classes. For example, Webb, Ing, Kersting, and Nemer (2006) found that 6th- and 7th-grade math students who received help during class performed better on math tests than students who did not ask for or receive help. In contrast, Ryan and Pintrich’s (1997) data failed to reveal a significant relationship between help seeking and performance on a standardized math test in a study of 7th- and 8-grade students. Karabenick and Knapp (1988) looked at help-seeking behaviors and academic performance beyond a single class. Looking at self-reported amounts of help seeking among university students, they found a curvilinear relationship between help seeking and grades such that help seeking was most prevalent among students with mid-level grades and least prevalent among students with either low grades or high grades. However, the grade measure in this study was a self-report of expected grades at the end of the term. The grades were also considered as markers of a need for academic assistance rather than a result of it. The researchers considered those with low grades as students in need of academic assistance and found that those most in need did not seek help. In this study, grades were used to predict help seeking instead of help seeking used to predict grades. Our study was conducted to see if help-seeking attitudes predict help-seeking behaviors and if help-seeking behaviors then predict grades.

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A Prospective Study Much of the help-seeking literature assumes that students who seek help will receive the help they need, that the help will be effective, and that the students will perform better in their academics as a result of the help. Much of the help-seeking research focuses on help-seeking behaviors as the dependent variable without testing if that behavior actually predicts academic performance. Although evidence suggests negative attitudes toward help seeking undermine actual help-seeking behaviors, the evidence is mixed concerning the extent to which help-seeking behaviors predict desired outcomes, especially prospectively. To test if students with an open attitude do seek more help and in turn, do get better grades, we evaluated the longitudinal relationships among attitudes toward help seeking, actual academic help-seeking behaviors, and academic performance among 119 first-year students at a small liberal arts college. In particular, we wanted to know if attitudes toward help seeking (as assessed at the end of the first semester of college; Time 1) predicted help seeking during the second semester of college (as reported at the end of the second semester; Time 2), and whether help seeking during that second semester predicted academic performance during that same semester. During the Time 1 assessment, which took place at the end of the students’ first semester of college, we assessed attitudes toward help seeking using a modified version of the avoidance of help seeking subscale of the Psychometric Analysis of Computer Science Help-Seeking Scales (Pajares, Cheong, & Oberman, 2004). These questions assess to what extent the participant avoids asking for academic help when he or she struggles with material. The scale measures the construct of avoiding help seeking and therefore encompasses, but is not specific to, the students’ concerns underlying the avoidant attitude (e.g., ability concerns). Items from the original scale asked about help seeking in a computer science setting; we modified the items to address help seeking more generally. For example, we modified the item that originally read “If I need help to do a computer science problem, I prefer to skip it rather than ask for help” to read “If I need help to do a problem, I prefer to skip it rather than ask for help.” Not surprisingly, some students held very favorable attitudes toward help-seeking, while others clearly avoided help seeking whenever possible. Then, at the Time 2 assessment, which took place at the end of the students’ second semester of college, we asked the students to self-report on their help seeking behaviors during the previous semester. We wanted to know how many times per month they did things like contact professors via email, talk to professors one-on-one, visit a tutor for assistance, and seek academic assistance from peers. Students varied a great deal in their tendency to seek help; some never sought help and others sought help 95 times from peers. By far the most common form of help seeking involved peer-to-peer support (modal response 10 times per month),

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though students also reported emailing their professors with some regularity (modal response two times per month) and visiting professors in office hours (modal response once per month). Most students reported never visiting a tutor as a form of help seeking. With students’ permission, measures of academic achievement for their first year of college were collected from the college registrar. The college does not calculate grade point averages (GPAs) for students’ first semester of school. Instead, students receive a “high pass,” “pass,” or “no pass” for each class. We used the number of high passes as a Time 1 measure of academic achievement. The average number of high passes was about 2. Grade point average (GPA) was collected from the registrar at the end of the year (Time 2). GPA at the end of the year averaged 3.0.

Help-Seeking Attitudes Predict GPA We expected that help-seeking behaviors would mediate the relationship between attitudes toward help seeking and GPA. That is to say, the relationship between attitudes toward help seeking and GPA would be explained by actual help-seeking behavior. To test this relationship, we first analyzed the relationship between avoidant attitude and academic performance. A Pearson’s correlation revealed that there was a significant negative relationship between avoidant attitude at Time 1 and GPA at Time 2; the more avoidant students were at the end of the first semester, the lower their grades were at the end of the second semester. We then analyzed the relationship between attitudes and behaviors. We found significant negative correlations between avoidant attitudes at Time 1 and most of the help-seeking behaviors assessed at Time 2; the more avoidant students were at the end of the first semester, the less they sought help during the second semester. However, there was no significant relationship between these help-seeking behaviors measured at Time 2 and GPA at Time 2, that is to say, higher levels of seeking help were not related to higher grades. Although attitudes toward help seeking predicted academic performance, actual help seeking did not (interested readers can view bivariate correlations among variables in Table 6.1). To understand these surprising results, we explored the possibility that a positive effect of help seeking only exists for those students who actually need help, as indicated by a history of low academic achievement. To test this possibility, we examined the relationships among attitudes toward help seeking, actual help seeking, and academic achievement for students whose academic record prior to Time 2 indicated low achievement. Specifically, we limited the data set to students who received zero or one “high pass” grade during their first semester of college. Yet even after using only the students who had lower achievement (those who

.25

.27



.42

.28

.29



.08 .10

.18†

.19


– .14

Visiting a tutor (Time 2)

.09

–

Seeking assistance from peers (Time 2)

Notes; †p ¼ .07
p < .05

p < .01. 1To maintain assumptions of normality, the help seeking measure and the avoidant attitude measure were transformed by taking the square root of the responses.

.25



–

.49



.01

Talking to professors one-on-one (Time 2)

–

Contacting professors via email (Time 2)

.08

Cumulative GPA (Time 2)

Bivariate correlations among help-seeking behaviors1

Contacting professors via email (Time 2) Talking to professors one-on-one (Time 2) Visiting a tutor (Time 2) Seeking assistance from peers (Time 2) Avoidance of help seeking (Time 1)

Table 6.1

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should benefit the most from seeking help), the measures of help-seeking behavior were still not significantly related to Time 2 GPA. An alternative approach to accounting for previous academic performance is to use a control variable in regression analysis. We conducted such analyses using number of high passes in the previous semester as a control variable. This analysis revealed a similar pattern of findings: three of the four help-seeking behaviors failed to significantly predict Time 2 GPA (contact professor via email predicted GPA in this circumstance). Three key findings emerged from this study. First, consistent with earlier research, attitudes about help seeking predicted actual help seeking. Specifically, students who held more avoidant attitudes toward help seeking reported seeking less help in the subsequent semester than students with more positive attitudes. Second, the data fail to support the assumption that help seeking leads to more favorable academic outcomes, at least at this broadly measured level. Third, and most interesting given the lack of association between actual help seeking and academic performance, is this: avoidant attitude in the middle of the year was predictive of lower grades in the following term. Taken as a whole, our data suggest attitudes about help seeking are more important than actual help-seeking behaviors when considering academic achievement as measured by GPA.

Explaining the Findings There are several possible explanations for the surprising finding that help seeking behavior was unrelated to grades. First, the items we used to measure help seeking may have seemed ambiguous to the students. For example, two of the four items that measured help seeking did not use the specific words “for help” but asked about frequency of communication with a professor, either via email or personally. It is possible that these items did not predict grades because they measured communication, perhaps in addition to help seeking. Although this is a possibility, even those measures that asked specifically about help seeking (peers and tutors) did not predict grades, and the ones that didn’t include the words “for help” were still significantly correlated with attitudes toward help seeking. If these items lacked the specificity needed to predict GPA then that same lack of specificity should render them un-correlated with attitudes toward help seeking as well. Second, other venues for help (e.g., from parents or mentors) were not included in the survey. Help seeking could be evidenced in much subtler ways such as asking a question in class or going to the library to look things up. Including more helpseeking behaviors could be an area of future research. A third explanation for the lack of relationship between help-seeking behaviors and GPA is an untested variable like quality of the help received. The study considers self-report data from students indicating the extent to which they sought

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help over the previous month; however, we did not gather data on the quality of help the students received. Students might have asked help from a peer or professor who was ultimately unhelpful. Failing to receive appropriate and high-quality help after having sought it could explain the lack of relationship between help seeking and academic performance. Measuring the quality of help received could be another area for future research. Although there are several possible reasons for why we did not discover a relationship between help seeking behaviors and grades, this does not detract from the interesting finding that student attitudes toward help seeking is a good predictor of GPA. Based on previous research, we suggest several possible explanations for this finding. First, social support processes explain the relationship between attitudes toward help seeking and academic achievement. As suggested by the items from the avoidance of help seeking scale, help seeking typically requires interaction with other people; it is a distinct possibility that those who are open to asking for help might also simply have more interaction with others. Thus, the avoidance of help seeking measure could be related to social support. Consistent with research indicating social support at the collegiate level is a significant predictor of academic achievement (DeBerard, Spielmans & Julka, 2004), a hidden component of social support in our measurements may account for the observed relationship between help-seeking attitudes and GPA. The explanation we favor for the attitudinal measure being a better predictor of GPA than actual help seeking behavior is that the measure of help-seeking attitudes is also a measure of attitude toward learning in general. Some of the questions in the Pajares et al. (2004) subscale address the notion of trying to do things by oneself and being the one to get the answer. In many subtle ways, help seeking is a component of learning; raising one’s hand in class, researching on the Internet, or referring to textbooks are fundamentally ways of getting help. All these actions require receptivity to learning new things. If this is the case, then it might be this receptiveness to learning that predicts good grades regardless of the more concrete help-seeking behaviors in which students might engage. This explanation places an emphasis on the importance of appropriate attitudes. Such an explanation would be supported by previous research on the behavioral implications of attitudes and perceptions. For example, research on locus of control reveals perceptions of personal control over life events (as opposed to fate or chance) is related to positive outcomes like life satisfaction (Verme, 2009) and self-report measures of health (Johnson, Batey, & Holdsworth, 2009). In the area of student learning outcomes studies have found that students with an internal locus of control are more likely to make good grades (Grigorenko et al., 2009). Clearly, attitudes can affect academic achievement outcomes. A receptive attitude toward learning may be another such attitude. This explanation of a receptive attitude may also correspond to the autonomy concerns that are involved in student attitudes toward help seeking. Students who

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are not receptive to learning from others, and who instead wish to do things all on their own, are often concerned with autonomy and feel threatened by help seeking (Butler, 1998). The idea that autonomy concerns drive the relationship between avoidant attitudes and lower academic achievement measures is supported by Karabenick’s research (2003). In a survey of chemistry students, Karabenick asked how threatened they felt by help seeking in that class. He found that college students with avoidant tendencies (construed as feeling threatened by the act of help seeking) in the middle of the semester had lower exam scores than those who did not have these avoidant tendencies. Like our study, this research shows students doing well when they are less concerned about autonomous achievement. Future studies could employ more precise questions to measure a receptive attitude rather than avoidant attitude to explore this explanation.

Recommendations for Educators Students may arrive to college with a desire to prove to themselves and others that they are capable of achieving academically on their own, without the help of others. This desire might be so strong that they don’t seek help even when they need it, and that they in fact develop a negative attitude toward help seeking. Our research suggests that such avoidant attitudes toward help seeking are not adaptive in the college environment. This conclusion is not surprising given the emphasis many institutions of higher learning place on the shared nature of learning. We hold conferences, require group work, ask our students to give presentations, and organize discussion-based classes. Colleges often push students to learn through interaction with peers and professors. Learning and an openness to receiving help go hand in hand. Thus an attitude toward learning that is focused on individual performance not only leads to less help seeking (Ryan et al., 2001) but is also related to lower GPA (current study). Since the results of this study are correlational, more research should be conducted to help sort out the direction of causality. Meanwhile, instructors and administrators who desire to improve academic achievement among their students can still apply what we know so far. Educators may consider addressing performance concerns and emphasizing the collaborative nature of learning. For college administrators this might be done by incorporating a discussion of attitudes toward learning into programs for freshmen. During freshmen orientation a block of time could be set aside to talk to students about the learning environment of the college. During this time, new students can be informed of the ideals of collaboration and cooperation. Skits or case studies of successful and unsuccessful students may be used so that new students may identify their own attitudes toward learning and help seeking. Another option would be to give students an avoidance of help seeking scale and allow them to self-score.

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This self-knowledge may help students move to an attitude that is less concerned with autonomy and performance and more receptive to learning with the help of others. Teachers and professors can also encourage a receptive attitude and discourage performance-focused attitudes in their own classes. They may do this by modeling the more positive attitudes. When asked questions they cannot answer, teachers may honestly admit that they do not know the answer and guide the student in the process of how to find the answer. For example, teachers may reply; “how would we go about finding the answer to that?;” “who do we know that would be able to answer that question?;” “I think we can figure out the answer together, what information do we need first?” By responding with a willingness to learn, students will see that successful scholars are those who are open to learning with the help of others. Another way for teachers and professors to encourage a receptive attitude is to form study groups in class or to require one-on-one sessions with the teacher outside of class. Giving students the opportunity to experience learning with the help of others may help to inculcate a positive attitude toward the experiences. At a young age we applaud students for doing things “all by themselves,” but as students mature, educators should remind them that the nature of scholarship is a collaborative effort and achievement can only be effected by “standing on the shoulders of giants.”

Acknowledgments This research was supported by a Beckman Research fund awarded to the fourth author by Harvey Mudd College.

References Butler, R. (1998). Determinants of help seeking: Relations between perceived reasons for classroom help-avoidance and help-seeking behaviors in an experimental context. Journal of Educational Psychology, 90, 630–643. DeBerard, M. S., Spielmans, G. I., & Julka, D. L. (2004). Predictors of academic achievement and retention among college freshmen: A longitudinal study. College Student Journal, 38(1), 66–80. Grigorenko, E., Jarvin, L., Diffley, R., Goodyear, J., Shanahan, E., & Sternberg, R. (2009). Are SSATS and GPA enough? A theory-based approach to predicting academic success in secondary school. Journal of Educational Psychology, 101(4), 964–981. doi: 10.1037/ a0015906 Johnson, S., Batey, M., & Holdsworth, L. (2009). Personality and health: The mediating role of trait emotional intelligence and work locus of control. Personality and Individual Differences, 47(5), 470–475.

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Karabenick, S. A. (2003). Seeking help in large college classes: A person-centered approach. Contemporary Educational Psychology, 28, 37–58. Karabenick, S. A., & Knapp, J. R. (1988). Help seeking and the need for academic assistance. Journal of Educational Psychology, 80, 406–408. Karabenick, S. A., & Knapp, J. R. (1991). Relationship of academic help seeking to the use of learning strategies and other instrumental achievement behavior in college students. Journal of Educational Psychology, 83, 221–230. Nadler, A. (1983). Personal characteristics and help-seeking. In B. M. DePaulo, A. Nadler, & J. D. Fisher (Eds.), New directions in helping: Help seeking (Vol. 2, pp. 303–336). New York: Academic Press. Pajares, F., Cheong, Y. F., & Oberman, P. (2004). Psychometric analysis of computer science help-seeking scales. Educational and Psychological Measurement, 64, 496–513. Ryan, A. M., Patrick, H., & Shim, S. (2005). Differential profiles of students identified by their teacher as having avoidant, appropriate, or dependent help-seeking tendencies in the classroom. Journal of Educational Psychology. 97(2), 275–85. Ryan, A. M., & Pintrich, P. R. (1997). “Should I ask for help?”: The role of motivation and attitudes in adolescents’ help seeking in math class. Journal of Educational Psychology, 89, 329–341. Ryan, A. M., & Pintrich, P. R. (2006). Achievement and social motivation influences on help seeking in the classroom. In S.A. Karabenick (Ed.), Strategic help seeking: Implications for learning and teaching (pp. 13–37). Mahwah, NJ: Lawrence Erlbaum Associates. Ryan, A. M., Pintrich, P. R., & Midgley, C. (2001). Avoiding seeking help in the classroom: Who and why? Educational Psychology Review, 13, 93–114. Verme, P. (2009). Happiness, freedom and control. Journal of Economic Behavior & Organization, 71(2), 146–161. doi: 10.1016/j.jebo.2009.04.008 Webb, N. M., Ing, M., Kersting, N., & Nemer, K. M. (2006). Help seeking in cooperative learning groups. In S. A. Karabenick & R. S. Newman (Eds.), Help seeking in academic settings (pp. 225–258). Mahwah, NJ: Lawrence Erlbaum Associates.

7 Applying the Science of Learning to the Art of Teaching Diane F. Halpern and Clayton L. Stephenson

We want the reader to consider some staggering numbers about the exchange of information in the year 2009 and the impact it has had on different facets of people’s lives. For those of us old enough to remember life before Google, the rate at which people are consuming data is unimaginable. Over 9.1 billion searches were made on this Internet search engine in March 2009, which is an average of 300 million searches per day (Oreskovic, 2009). Add to this number the over 2 billion searches on Google’s main competitor, Yahoo!, and the countless searches being done in multiple languages on other search engines around the world and the numbers are mind-boggling. It seems that almost everyone is exchanging information with dizzying speed using new ways to communicate. It has been estimated that 2.3 trillion text message will have been sent by early 2010 (Moskalyuk, 2006); quite an achievement for a communication medium that came into popular use less than two decades ago. The number of people on Facebook, the ubiquitous social network, is growing so fast that even the best estimates are out of date within a few days. Estimates of the number of people on Facebook in 2009 range from 29 million (Inside Facebook, 2009) to 175 million (Facebook blog, 2009). If the latter claim is true, and if Facebook were a country, it would have the sixth largest population in the world. Finally, consider how the frenzy of information exchange has also changed the nature of work, and not surprisingly, people also changing jobs with increasing frequency. Longitudinal studies conducted by the US National Labor Bureau show that between the years 1979 and 2006, adults aged 18 to 42 held an average of 10.8 jobs after becoming an adult! What’s wrong with these astounding numbers? The biggest problem is that we do not know the accuracy of any of the numbers we just presented, and the

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credibility of the sources from which we got these data varies greatly. We trust data about the number of jobs adults have that were presented by the National Labor Bureau because of the extensive documentation the Bureau provided on how the data were collected, and its impressive encyclopedia of tables suggest that care was taken to provide reliable data. But what about the other numbers we just presented? We do not doubt that there are a large number of searches on Google or that millions of people have Facebook pages, but one clue that the data we presented are suspect is the wide range in the number of estimated users. Somewhere between 29 million and 175 million is too wide a range to be meaningful, and we have no good way to determine which of the online sources we used for these data is more reliable or if either one can be considered valid. Yet, regardless of which number we present to colleagues about the rapid pace of change, they nod their heads in agreement. Thus far, none have questioned our data sources or the accuracy of the data. We assume that many people expect that we would not be presenting data that are questionable, but the only source we have for these numbers is a variety of Internet sites, and it is often difficult to determine the credibility of the site. The information frenzy, regardless of whether we have good estimates of the actual number of exchanges or not, is radically changing what students need to know and be able to do as they prepare for work and family life in the 21st century. We hope that our opening remarks show the need for being able to judge the credibility of information and being mindful about accuracy when numbers are presented. Despite the fact that today’s students need new skills to match the changes in the way they obtain information, far too many college faculty continue to teach as they have been taught. If you were a college student in the 1970s who was assigned a term paper for a psychology course, you would have had to access information by physically going into the library, looking up sources in the massive tomes called Psychological Abstracts, walk into the periodical stacks, and, most likely, read the article while in the library, assuming that the library actually owned a copy of the article. By the late 1990s students could look up articles on an electronic search engine such as PsychInfo. For most students in the last decade of the 20th century, the electronic world met real life as they trudged to the periodical stacks to retrieve the article, which could easily be copied with machines that were available at a cost throughout the library. Currently, in the year 2010, students can search and get research articles from the comfort of their home. Because of extensive information services, most of what these students are searching for can be found in a few clicks of a mouse. The savings in time and money is taken for granted by today’s students whose eyes glass over when we talk about the old days when trips to the library were always uphill and in the snow. Finding sources for a paper is easily accomplished partly because there are approximately 500 billion gigabytes of data available on the Internet, which would be the same as stacking books from Earth to Pluto ten times over

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(Wray, 2009). Skeptical readers should be immediately questioning this number, which came from an online version of a reputable newspaper and was written by a psychological scientist, which makes it more credible than information from a tabloid or random blog, but not as credible as a publication in a peerreviewed journal. People are finding information on just about anything they want or need. However, it cannot be assumed that just because people have so much information available to them it means that they know how to use and think about the information correctly. Information has to be credible, and consumers of information, which includes everyone, need to know how to think about the massive amounts of information or all that information is no more useful on their home computer screen than it is locked away in a library on the other side of the world. Now, more than ever, we need to teach students how to think about the dizzying array of information that is accessible in one or two mouse clicks. In other words, we need to teach for critical thinking.

What is Critical Thinking? Critical thinking is the use of those cognitive skills and abilities that increase the probability of a desirable outcome. It is purposeful, reasoned, and goal directed. It is the kind of thinking involved in solving problems, formulating inferences, calculating likelihoods, and making decisions (Halpern, 2003). Critical thinkers use these skills appropriately, without prompting, and usually with conscious intent, in a variety of settings. That is, they are predisposed to think critically. When we think critically, we are evaluating the outcomes of our thought processes—how good a decision is or how well a problem is solved. Critical thinking also involves evaluating the thinking process—the reasoning that went into the conclusion we have arrived at or the kinds of factors considered in making a decision. Thus, in a world where we are drowning in information, students need the skills to think critically and the disposition to use those skills.

Transfer of Critical Thinking Skills Everything that happens in college classrooms, or any other classrooms at any level of education for that matter, is designed for one purpose—transfer of learning beyond the classroom to the real world settings where people live, work, and play. In general, transfer can be thought of as ranging from near and far, with near referring to contexts and topics that are similar to those being taught and far referring to contexts and topics that are dissimilar to what is being taught (Royer, Mestre, & Dufresne, 2005). An example of near transfer is the application of a mathematical problem solving technique that was learned in school to a similar

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math problem that is assigned at school. Far transfer involves use of a skill across time, place, and topic. For example, if the same math problem solving technique were used two years after it was learned in an employment setting with a problem that differs from the ones used in learning, the student would have demonstrated far transfer. There is no sharp dividing line between near and far transfer; all types of intermediate transfer also occur. Far transfer is a more advanced level of critical thinking and refers to learners’ ability to use critical thinking skills in real world contexts. Far transfer is especially important when people need to make medical or consumer decisions because these topics are rarely taught in schools. For example, a student in a critical thinking course may learn about the influence that language has on thought. This student later reads about the H1N1 Flu virus (i.e., Swine Flu), which was a new string of the flu virus and was reported as a virus that could become the next “global killer” with news titles such as “Officials Say Flu Cannot Be Contained as Cases Rise” (Walsh, 2009). In June of 2009, the H1N1 Flue was declared to be a global pandemic (Center for Disease Control and Protection, 2009). The term “global pandemic” is a loaded term and can strike fear in people, especially when the news reports how many people have died from the new virus. The person who demonstrates far transfer of critical thinking skills will be able to recognize that the words and phrases used by the news may be accurate, but they are used to grab attention and increase readership. The person who can transfer critical thinking skills that were learned in class will first read the article and then look up the definition of a global pandemic (it is based on how rapidly a new disease spreads, not the severity of the disease) and, as a result, may not make rash decisions about how to avoid or treat H1N1 that could have a negative effect on finances or lifestyle. People who demonstrate far transfer will also realize that the number of deaths, but not the total number of cases is usually given in a news report. As of July 1, 2009, the World Health Organization (WHO) reported 77,201 cases of the H1N1 flu and 332 reported deaths as a result, which translates into a 0.4% kill rate. Based on this information, people who use critical thinking skills in the real world may come to the conclusion that a “kill rate” of less than half a percent of those infected is not large enough to warrant drastic life changes, such as quitting one’s job to remain in the house as a way of avoiding human contact or trying untested medical treatments. There has been pressure put on instructors to produce learners who can demonstrate far transfer of critical thinking skills and information (De Corte, 2003). Although it is admirable to strive for and obtain far transfer, it does not come easily. Common issues in training and classroom learning that work against transfer include too much information presented at once (i.e., cognitive overload), low learner motivation, and teaching for immediate retention on exams rather than long-term retention. There are, however, principles of learning that can help promote far transfer of critical thinking skills and information.

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Principles of Learning that Promote Transfer We have grouped learning principles that promote transfer into seven broad categories. Many of these principles are listed in Graesser (2009). Our list in not exhaustive, but it contains those principles that researchers have found to have the strongest effect on enhancing learning and memory. Furthermore, the principles listed below are ones that can easily be adapted by instructors and learners in or out of the classroom.

Clarify Objectives Both teachers and students can find the teaching-learning process stressful when neither has a clear understanding of what is supposed to be learned. Much of that stress can be relieved by using the principle of clarifying objectives. A statement of learning objectives is a straightforward way of describing what the learners are supposed to know and what they will be able to do with the information after it is learned. Learning objectives can be prefaced with the statement, “After successfully completing this course, students will be able to. . ..” After the preface, the objectives are listed using active verbs to describe what the student will be able to do (Richlin, 2006). Using active verbs tells the students where they are intellectually supposed to be at the end of a lesson, an entire course, or a training session. In addition, it sets a stage for constructing measurable outcomes of whether or not the learner reached that expectation. Basic research on memory and learning has shown that knowing what one is expected to remember increases retrieval. For example, Szpunar, McDermott, and Roediger (2007) had participants study a list of words and their memory was tested for those words. One group of participants was made aware during the initial testing that the words would be on a final testing while another group was not made aware of that they would be tested later. Szpunar et al. found that the group that was made aware that their memory for the list of words would be tested, outperformed the group that was not made aware by recalling a higher percentage of words. Of course, in real classrooms and training sessions, learners are expected to learn more complex information than lists of words, so the principle of being clear about what is to be learned is even more important when the desired learning outcomes can be hard to tease from a class where a great deal of information is presented. Szpunar et al.’s research supports the idea that clarifying the objective of a learning session allows the student to focus on the information to be learned rather than using resources to figure out what they should be focusing on. Bloom’s taxonomy of educational objectives has been a popular source for creating learning objectives (Marzano & Kendall, 2007; Richlin, 2006). The two primary dimensions of Bloom’s Taxonomy are the knowledge dimension and the cognitive process dimension. The knowledge dimension consists of four levels

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of knowledge: Factual; Conceptual; Procedural; and Metacognitive. The cognitive process dimension consists of six levels: Remember; Understand; Apply; Analyze; Evaluate; and Create. The levels of each dimension increases in difficulty and should be chosen carefully based on the difficulty level of the material and the learner’s cognitive ability and developmental stage. Here is an example of learning objectives for a writing course that uses Bloom’s taxonomy: a) develop a writing schedule and writing style to improve their scholarly writing; b) construct and critique academic arguments; and c) generate well written research papers for this course and any other future course taken. Another benefit of creating learning objectives is that the design of learning experiences, reading assignments, homework, and tests derive from the objectives; thus, it should be easier for instructors to decide what to include and what to leave out when planning the course and planning assessments.

Have Students Generate Responses One of the most robust findings from the research literature in cognitive psychology is that recall of information improves when learners generate the to-be-remembered information under conditions with minimal retrieval cues. In the research literature, information in memory is often called a memory trace. Each memory trace has an associated strength or probability that it will be recalled. According to standard “memory trace” theories of how we remember, the act of remembering strengthens some memory traces and weakens, or perhaps fails to strengthen, others; a fact that should influence how we test students. When students practice retrieval, the strength of the memory trace is increased and the shape of the forgetting curve is altered, which reflects the fact that the just-retrieved information is now more likely to be retrieved (i.e., less likely to be forgotten) in the future. In more common language, this principle is sometimes called the “testing effect,” which refers to the finding that by responding to a question on a test, the learner is also practicing retrieval, thus making later retrieval of the same material more likely to be successful (Cull, 2000; deWinstanley & Bjork, 2002; Halpern & Hakel, 2003). Students must be actively processing information when generating responses. More passive learning techniques such as listening to a lecture or following a PowerPoint presentation do not allow the learner to invest in the information. These more passive situations also create an unreliable feeling that the learner knows the material because it becomes familiar, but familiarity is not good enough to produce learning that lasts. For example, if an instructor is showing a video to the class and expects students to remember what they are viewing, it would be beneficial to have the students fill out a handout that asks questions relevant to what the instructor wants students to learn. The questions act as retrieval cues, and the memory for the information is strengthened by generating responses.

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In addition to questions, retrieval cues can come in the form of fill in the blanks, concepts maps that need to be drawn, or even word fragments that need to be completed. Tests can also be designed to develop long-term retention through the generation effect by using more free recall and essay questions that contain minimal cues. There are two important factors to consider when having learners generate their own answers: deep questioning and explaining. When the questions are relatively shallow, such as asking student to write the equation for acceleration, the learning that results are also shallow. But when the questions call for deeper level processing, such as determine which car accelerates faster based on the information provided about the cars, then the learning is also deeper. Learners can also be taught to ask their own deep questions as they read material, instead of reading the material over and over as a learning technique. By asking their own deep questions, students are also developing their metacognitive skills. They need to make judgments about the quality of their learning and change strategies when they do not comprehend the material. Deep questions often require explanations. A learner can formulate deep questions about their own thinking and learning such as, “Why am I having a hard time learning this material? What do I need to change in my approach to learning this material?” Test-enhanced learning occurs because it requires learners to generate responses. According to Roediger, McDaniel, and McDermott (2006, para. 3) tests of all kinds are useful, but “the short answer test, requiring students to produce answers, led to a greater benefit on the final test regardless of the nature of the final test (as long as feedback was given on the test). This finding has parallels to work from the list-learning literature showing that recall tests produce a greater testing effect than do recognition tests.” When students are explaining, they need to analyze their understanding. They may need to find plausible causal links for events, provide logical justifications for alternatives, and present counterarguments for arguments and rationales for actions. When students explain, they are creating a deeper understanding of the material and additional cues for retrieval. Learners can be taught to develop selfexplanations as a means for promoting deeper comprehension while reading, which will result in more durable learning and better transfer. For example, an important piece of American history includes the creation of Labor Day. Instead of asking the learner to memorize when and where the first Labor Day was celebrated, it would be better to ask the learner what events led to the creation of Labor Day, who was involved, and why the original date of May 1 did not remain the official celebratory day. This type of questioning allows the learner to make causal links between events that may not seem obvious and to create plausible arguments for the context in which Labor Day became a holiday. Explanations create a story, and stories have a privileged status in memory (Graesser, Olde, & Klettke, 2002).

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Distribute Learning The principle of distributed learning has a long history in psychology (Donovan & Radosevich, 1999). It is one of the most robust principles for promoting long-term retention and transfer. Unfortunately, it is rarely practiced by learners who typically cram the night before a test or by teachers who cover material once and would consider it a waste of time to review material that was already taught. With distributed learning, learning sessions are spaced out over time, with review of earlier learning occurring along with the presentation of new information. The spaced intervals allow the learner to manage cognitive load and, in this way, they promote long-term retention. Consider the following topics that students may need to learn for a test in a critical thinking course: Inductive reasoning, deductive reasoning, hypothesis testing, populations, samples, independent variables, dependent variables, reliability, and variability. Students should start studying the information well in advance of the test and should study at regular intervals throughout the term. The first day of study can be spent studying inductive reasoning for one hour. The second day could be spent studying deductive reasoning for 50 minutes while the other 10 minutes are spent reviewing inductive reasoning. The remaining days would follow a similar pattern so that when the day before the test arrives, the person is spending the majority of the study session reviewing information instead of frantically learning it by cramming. While the cram sessions for a single test may lead to an immediate memorization of the material, learners who distribute their learning have a higher test performance outcome, retain the material for a longer period of time, and are more likely to transfer the material to novel situations. Pashler, Rohrer, and Cepeda (2006) summarized the research literature on distributed practice: The benefits of spacing seem to grow ever larger as retention intervals are lengthened; thus, for one-year retention, a one-month spacing produces a threefold or greater increase in memory as compared to a day or even a week of spacing. While increasing spacing too much always produces some decline, as earlier shortduration studies had implied, the decline is invariably quite modest. Therefore, to facilitate retention over years, it seems critical to space training over several months at least.

Based on the evidence for distributed learning, instructors are encouraged to begin each class period by reviewing topics that were learned in past lessons. The time devoted to this activity does not have to take so long as to take away time from learning new material; rather, five to ten minutes of review in the beginning of class is adequate if the review session promotes deep learning. For example, if a history teacher is teaching colonialism in America, then it would be useful to review previous material for the first few minutes of class or have the students

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work with material that was previously learned to help make connections and build an overall picture of why, how, and who was affected by colonialism in America.

Vary Learning Activities The theme of variability during learning underscores the causal and sometimes contradictory relationship between what happens during learning and long-term retention. Learners need to expend more effort when learning conditions are variable and, thus, they report that the learning was less enjoyable. But, the hard work of effortful learning pays large dividends when long-term retention is assessed. Consider, for example, that one commonly accepted hallmark of good teaching is the clear organization of the information to-be-learned. Students rate learning experiences especially high when the sequence and organization of information presented in class matches that of the text, probably because the learning process is easier when the organizational structures match. Unfortunately, the easiest learning often does not create the least resistance to forgetting. It seems that information is remembered longer and the learning itself is “deeper” when learners are required to exert the effort of creating their own organizational structures (Simon & Bjork, 2002). In a classic text in cognitive psychology, Sir Frederick Bartlett (1932) used the term “effort after meaning.” He was talking about effort in pursuit of meaning, thus the principle of effortful learning was known early in the last century, yet it is routinely ignored as a general principle for teaching and learning. We want students to be able to retrieve information in a variety of settings where the information is relevant and to be able to use information in novel ways. We are describing the concept of cognitive flexibility, which is the ability to adapt one’s thinking and use of knowledge to a variety of situations. Cognitive flexibility increases when learning activities are varied (Spiro, Feltovich, Jacobson, & Coulson, 1991). For example, material about controversial health care plans could be viewed from a variety of perspectives—an uninsured parent of a sick child, a business owner who is worried about costs, a physician who is concerned with quality health care for the poor, a working adult with good health care who is worried about losing the plan that she now has, and so on. Different facts and skills will be more relevant for the differing perspectives, and the “whole” of the information can come together more completely when it is constructed from different perspectives. Learning should also take place in different settings so that recall is not tied to a particular place or person. The teacher can become a recall cue, so that students can use knowledge and skills effectively when the teacher is present, but the same knowledge and skills may not be available to the student when the student is in a different setting and the teacher is not there.

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A course on sensation and perception is a class that has endless possibilities in providing students with a variety of learning activities. For example, visual illusions tell us a great deal about the way the visual systems functions. Students can engage in color and light mixing to better understand color mixture, and they can determine if they qualify as “super tasters” with a simple test that allows them to count their taste buds.

Use Dual-Coding How can we think about the information we are currently thinking about? This question is probably strange to those of you who are not familiar with cognitive theories and research, but to those of us in the field it is a straightforward question about the representation of meaning in working memory. For example, suppose we asked you to engage in two tasks simultaneously during a lab study. One task is for you to imagine the route between the front of the university and your office. The second task is to learn how to recognize shapes that do not have readily available names (i.e., not a rectangle or pentagon). If you do the two tasks at the same time, you would find that the two tasks interfere with each other because you are using a visuospatial code to perform both tasks. In general, memories are more durable when we use both visuospatial (i.e., visual images) and verbal/auditory (i.e., verbal information) information when learning. Another common and simple demonstration that shows the distinction between visuospatial and verbal codes is to learn two lists of words by using imagery when learning one list and repetition to learn the other. The imagery list will be recalled more easily and accurately, in part because it was learned with two codes—the imagery code is visuospatial and the sound of the word is verbal/auditory. The previous two examples illustrate the dual-coding principle: people that are presented information using both the visual and auditory channels tend to remember the information better than when they are presented with information that relies on either spatial or verbal codes alone. The principle of dual-coding can be used in every learning situation to enhance learning. Any text—whether it is a psychology textbook or journal article or literary masterpiece—can be learned better if learners use spatial arrays such as concept maps to map relationships among concepts and to show hierarchical or other structures. Similarly, any spatial information, such as a geometry or physics problems can be studied with verbal codes. Students in mathematics can be asked to write why they selected the formula they are using or how one problem is similar or dissimilar to another problem. The principle of dual coding should also be used in the design of multimedia learning materials. As noted above, when two tasks require the use of the same coding strategy, the tasks interfere with each other and the result is poorer performance. Based on this reasoning, Mayer (2005) and his colleagues conducted

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numerous studies showing that visual information such as a diagram in an online multimedia learning program should be paired with an auditory explanation of the diagram instead of a text-based explanation, which would require the learner to process two visual bits of information (i.e., read the explanation while studying the diagram). Mayer systematically investigated the implications of dual coding and, based on a review of multiple studies, he concluded that large advantages in learning and remembering can be obtained by a) using words and pictures together as compared to words alone, b) words and pictures should be presented simultaneously in multimedia learning materials, c) the words should be spoken as narration and not read from a screen, and d) learners should be actively involved in controlling the presentation rate of information for material presented online such as a video recorded lecture. There is a proliferation of multimedia learning programs being developed; yet, few of their designers have used even the basic concepts from cognitive psychology to enhance their effectiveness.

Provide Feedback that Informs Standard learning texts always emphasized the need for feedback or knowledge of results as an essential component for enhancing learning, but more recent reviews have shown that feedback is a complex variable that sometimes hinders learning and sometimes enhances it (Kluger & DeNisi, 1996, 1998; Qui~ nones, 1995). When data from many studies were combined in a meta-analysis, the overall effect size for feedback was considerable, but the authors also found that over one-third of the studies showed that feedback had negative effects on learning and performance. Feedback is important in that it provides information to the learner about his or her own performance, but the learner still has to derive meaning from it and it may be that the way learners interpret feedback is what determines when it will be beneficial. What does feedback about whether the performance was good or poor really mean to a learner? Is successful learning indicative of the intelligence or skill of the learner, the difficulty of the learning task, the way learning performance is assessed, or the amount of effort expended? Learning settings need to be designed so that the type of feedback is matched to the intended reason for the learning task (e.g., remember a term, distinguish between concepts, critique an argument). For example, if a student hands in a paper for a biology course and the paper contains multiple run-on sentences, then the instructor should point out one or two of the run-on sentences and offer a way to rewrite the sentences and let the student identify the remaining run-on sentences. If the instructor would have asked the student to go look up a definition of a run-on sentence, then the feedback would not provide a constructive way to correct the sentences. However, if an instructor is asking a student to recall a term such as define “run-on sentence,” and the student

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fails to do so, then the instructor should suggest a source where the student can take action by looking up the definition. Feedback needs to be understood in terms of its intended use so that learners do not interpret feedback as punishment, which leads to resistance to the information provided in the feedback. Most research reviews conclude that feedback should be given at appropriate intervals, probably with increasing length of the intervals as learning progresses (Winstein & Schmidt, 1990). Thus, early in the learning process, more frequent feedback is needed than later in the learning process. In this way, learners can become judges of their own performance and rely less upon external knowledge of results than they would with more constant feedback. Providing appropriate feedback is critical for learners to self-regulate their learning. Depending on the task, feedback should be provided in a timely manner while also being spaced throughout time. These two aspects may seem to contradict each other. Providing feedback in a timely manner means that feedback on a paper or exam is not given two months after it was turned in. Spacing feedback is important for two reasons. First, spacing feedback will help enhance long-term retention of the material. Second, providing too much feedback in one time period can cause confusion, cognitive overload, and may also decrease motivation. As a result, the learners may feel like the effort being put into the material is useless and may start to resent the instructor and the material. In addition to spacing feedback and providing it in a timely manner, feedback should be limited for two primary reasons. First, providing feedback that gives away all of the correct answers does not challenge learners or allow for selfcorrection during discovery learning. For example, when the learner is provided the results of a multiple choice quiz, the learner will most likely want to know what the correct answers were. Rather than providing the answers outright, without the learners thinking about or working with the material that was incorrect, it is better to have the learners find the answers on their own or to work in groups to figure out the correct answers. The second reason for limiting feedback is to help promote the learner’s metacognition. If learners are handed the correct answers, they will most likely use rote memorization to remember the correct answers without truly understanding the content for future tests or performance assessments. By searching for the correct answers, learners will more likely monitor their own learning after receiving feedback that disconfirmed their prior belief that they were correct. This self-correction process directs them on a path to seeking and, hopefully, understanding the material and why they were incorrect. For example, a learner who consistently makes the mistake of using run-on sentences in a paper should not be told where every single run-on sentence exists. Instead, the instructor should highlight the examples of the worst run-on sentences the learner used and have the learner find the other run-on sentences in the paper during the revision process.

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Challenge Learners’ Epistemology How do we learn? How do we explain difficulties in learning? It seems that the way professors and students answer these questions can have a large effect on the efficacy of a learning activity. Academic motivation is related to beliefs about learning. For example, many adult learners believe that they cannot “do math,” or understand science, or write poetry, or succeed in some other academic discipline or at some task. When learners are asked to explain what this means, their answer reflects a belief that learning should be easy, but when they learn in these disciplines, it is effortful. What most learners do not know is that learning and remembering involve multiple, interdependent processes. Some types of learning occur implicitly, that is without conscious awareness. Sometimes, learning is easy, and other types of learning are effortful, perhaps even painful and aversive. It is only after learners invest in the hard work of learning that additional learning in these fields becomes easy and more automatic. Many people maintain fixed beliefs about the nature of learning and these beliefs guide how they explain good learning and learning failures and, as predicted by expectancy theory, the willingness to engage in learning tasks (Levy, Stroessner, & Dweck, 1998). Beliefs about learning tend to be invariant across learning tasks and contexts, but there is no single set of learning principles that will always work or all-purpose explanation of success and failure. The best way to learn and recall something will depend, in part, on what it is you want learners to learn and recall as well as what they already know, and the learners’ own beliefs about the nature of learning. Instructors can enhance learning by having learners articulate their implicit beliefs about learning so they can be examined, and if desired, the learning task can include the learning of a new model of how people learn. When an instructor understands what the learners believe learning is suppose to be like or what the learners are interested in, then the instructor can contextualize the material to match, or at least closely match, the students’ interests. The learning should still be effortful and learning tasks should be designed to dispel incorrect beliefs, but if it is set up in a context that the learners can relate to, then the learners will be more willing to put effort into the learning task.

Conclusion and Future Directions Although Facebook has become an ever popular source for social networking, there are a number of scams that exist on Facebook. One scam that continuously pops up is so-called Intelligent Quotient (IQ) tests. Naturally, people are curious what their IQ’s are and take these tests. One specific scam takes a person through the test and then prompts them to provide a phone number for the person’s results to be sent to them via text messaging (Heussner, 2009). Most people, however,

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do not see the fine print that states monthly fees will apply and are charged for their bogus test results. Thus, there is a “double-whammy”—meaningless information at a financial cost. Good learners and thinkers would not fall for a bogus claim to measure their intelligence and would have a heightened sense of caution because they know that the new technologies have also brought new scams to consumers. By incorporating the principles we have provided in this chapter, instructors will provide the tools to help promote transfer of critical thinking skills that will help people avoid such scams and be active in their acquisition of information. The primary role of professors is to create learning activities that help students achieve learning that lasts and can be used flexibly in multiple settings. The data are clear. There are many activities that will help professors reach this goal. It is also clear that few professors deliberately apply what we know about best practices in learning in their classrooms. Given the realities of our students’ lives, we believe that all faculty need to help students develop the twin pillars of thinking critically and learning how to learn. We urge every reader to experiment with various combinations of the seven broad categories of learning principles that have been shown to enhance learning. We also urge every professor to develop the same empirical attitude toward their own teaching as they do toward their research. Experiment with the seven learning principles and share your successes and failures with other professors via peer-reviewed publications and papers. When all professors embrace a scientist-educator model, we will make great progress toward understanding how to help students achieve at their highest potential (Bernstein et al., 2009). We urge all faculty to “engage in continuous, iterative inquiry into the success of their instruction in generating appropriate learning, using that evidence to refine instructional practices in ways that enhance the success of future students” (Halpern et al., 2009, p. 164).

References Bartlett, F. C. (1932). Remembering. Cambridge, UK: Cambridge University Press. Bernstein, D., Addison, W., Altman, C., Hollister, D., Komarraju, M., Prieto, L., et al. (2009). Toward a scientist-educator model of teaching psychology. In D. F. Halpern (Ed.), Undergraduate education in psychology: A blueprint for the future of the discipline (pp. 29–45). Washington, DC: American Psychological Association. Center for Disease Control and Protection. (2009). Novel H1N1 flu: Background on the situation. Retrieved from http://www.cdc.gov/h1n1flu/background.htm. Cull, W. L. (2000). Untangling the benefits of multiple study opportunities and repeated testing for cued recall. Applied Cognitive Psychology, 14, 215–235. De Corte, E. (2003). Designing learning environments that foster the productive use of acquired knowledge and skills. In E. De Corte, L. Verschaffel, N. Entwistle, &

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deWinstanley, P. A., & Bjork, R. A. (2002). Successful lecturing: Presenting information in ways that engage effective processing. New Directions for Teaching and Learning, 2002, 19–31. Donovan, J. J., & Radosevich, D. J. (1999). A meta-analytic review of the distribution of practice effect: Now you see it, now you don’t. Journal of Applied Psychology, 84, 795–805. Facebook Blog. (2009). The Facebook Blog. Retrieved from http://blog.facebook.com. Graesser, A. C. (2009). Inaugral editorial for Journal of Educational Psychology. Journal of Educational Psychology, 101, 259–261. Graesser, A. C., Olde, B., and Klettke, B. (2002). How does the mind construct and represent stories? In M. C. Green, J. J. Strange, & T. C. Brock (Eds.), Narrative impact: Social and cognitive foundations (pp. 231–263). Mahwah, NJ: Lawrence Erlbaum Associates. Halpern, D. F. (2003). Thought & knowledge: An introduction to critical thinking (4th ed.). Mahwah, NJ: Lawrence Erlbaum Associates. Halpern, D. F., Anton, B., Beins, B., Bernstein, D., Blair-Broeker, C., Brewer, C., et al. (2009). Principles for quality undergraduate education in psychology. In D. F. Halpern (Ed.), Undergraduate education in psychology: A blueprint for the future of the discipline (pp. 161–174). Washington, DC: American Psychological Association. Halpern, D. F., & Hakel, M. D. (2003). Applying the science of learning to the university and beyond: Teaching for long-term retention and transfer. Change, 35, 2–13. Heussner, K. (2009, June 11). How smart are you? IQ test scams Facebook users. ABC News. Retrieved from http://abcnews.go.com/Technology/AheadoftheCurve/story? id¼7814344&page¼1. Inside Facebook. (2009). Tracing Facebook and the Facebook platform for developers and users. Retrieved from http://www.insidefacebook.com. Kluger, A. N., & DeNisi, A. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119, 254–284. Kluger, A. N., & DeNisi, A. (1998). Feedback interventions: Toward the understanding of a double-edged sword. Current Directions in Psychological Science, 7, 67–72. Levy, S. R., Stroessner, S. J., & Dweck, C. S. (1998). Stereotype formation and endorsement: The role of implicit theories. Journal of Personality and Social Psychology, 74, 1421–1436. Marzano, R. J., & Kendall, J. S. (2007). The new taxonomy of educational objectives (2nd ed.). Thousand Oaks, CA: Corwin Press. Mayer, R. E. (2005). Cambridge handbook of multimedia learning. New York: Cambridge University Press. Moskalyuk, A. (2006, December 12). 2.3 trillion text messages sent by 2010 [Web log message]. Retrieved from http://blogs.zdnet.com/ITFacts/?p¼12176.

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Oreskovic, A. (2009, April 15). Google widens lead in U.S. searches: comScore. Reuters. Retrieved http://www.reuters.com/article/idUSN1552010320090415. Pashler, P., Rohrer, D., & Cepeda, N.J. (2006) Temporal spacing and learning. APS Observer, 19, 19. Qui~ nones, M. A. (1995). Pretraining context effects: Training assignment as feedback. Journal of Applied Psychology, 80, 226–238. Richlin, L. (2006). Blueprint for learning: Constructing college courses to facilitate, assess, and document learning. Sterling, VA: Stylus Publishing. Roediger, H. L., McDaniel, D., & McDermott, K. (2006). Test enhanced learning. APS Observer, 19, 28–38. Royer, M. J., Mestre, J. P., & Dufresne, R. J. (2005). Framing the transfer problem. In J. P. Mestre (Ed.), Transfer of learning: From a modern multidisciplinary perspective (pp. vii–xxvi). Greenwich, CT: Information Age Publishing. Simon, D. A., & Bjork, R. A. (2002). Models of performance in learning multisegment movement tasks: Consequences for acquisition, retention, and judgments of learning. Journal of Experimental Psychology: Applied, 4, 222–232. Spiro, R.J., Feltovich, P.J., Jacobson, M.J., & Coulson, R. C. (1991). Cognitive flexibility, constructivism, and hypertext: Random access instruction for advanced knowledge acquisition in ill-structured domains. Educational Technology, 31, 24–33. Szpunar, K. K., McDermott, K. B., & Roediger, H. L. III. (2007). Expectation of a final cumulative test enhances long-term retention. Memory & Cognition, 35, 1007–1013. Walsh, B. (2009, April 28). Officials say flue cannot be contained as cases rise. Time Inc. Retrieved from http://news.google.com. Winstein, C. J., & Schmidt, R. A. (1990). Reduced frequency of knowledge results enhances motor skill learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 677–691. World Health Organization. (2009). Pandemic (H1N1) 2009 – update 56. Retrieved from http://www.who.int/csr/don/2009_07_01a/en/index.html. Wray, R. (2009, May 18). Internet data head for 500bn gigabytes. The Guardian. Retrieved from http://www.guardian.co.uk/business/2009/may/18/digital-content-expansion.

8 Which Strategies Best Enhance Teaching and Learning in Higher Education? John Hattie

A synthesis of 800 meta-analytic studies males clear “what works best” in improving student achievement in higher education. From these studies, three key strategies are identified that the best teachers employ for enhancing student achievement: having clear learning intentions and success criteria (goals), having a preference for strategies that emphasize student perspectives in learning particularly in meta-cognitive and student regulated learning, and seeking feedback as to the success of academics as teachers. The major underlying basis of this model is that when student learning becomes “visible” to the teacher this not only enhances the probability of student achievement but increases the quality of teaching. Thus, it is the social psychological constructs of the teacher and student that are both intertwined in making teaching successful.

The Achievement Continuum and the Appropriate Reference Point Before extracting a set of recommended strategies for enhancing student achievement, it is necessary to justify what we mean when we say a teaching strategy or intervention “works.” This section outlines the major overviews of what works best in higher education, introduces meta-analyses and effect-sizes, and then the data base used in the remainder of this chapter. The development of the four scholarships by Boyer (1990) and in particular his scholarship of teaching and learning (SoTL) has led to increased discussions and debates such that there are now journals and books dedicated to providing evidence on achievement and almost none of the evidence is negative—that is, it is rare to locate studies that show that this or that intervention leads to decreased Empirical Research in Teaching and Learning: Contributions from Social Psychology Edited by Debra Mashek & Elizabeth Yost Hammer © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33719-8

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achievement. As an example of the preponderance of positive effects, Tomcho and Foels (2008) completed a meta-analysis of 197 studies relating to teaching activities and methods at the tertiary level. Only 10 effects were negative (and even these were close to zero), and everything else showed an achievement gain! The overall effect of the teacher activities was .57 on student knowledge, .27 on student attitude, .39 on assessed student behavior or skills, and .31 on grade outcomes. One of the more unusual attributes of teaching and learning is that everything seems to work—almost every approach and every teacher appears to have evidence for improved student outcomes. Students however know the diversity of how academics teach, prepare, assess and interact—and they claim that some are woeful, many are competent, and some are stunning. So why the disconnect between what is experienced by students and what is argued by academics? Everything does not work! Effect-sizes refer to the size or magnitude of differences that can be related to either the change over time (e.g., start to end of a course) or comparison between two methods (e.g., a teacher uses one method and a different teacher uses another method). The differences between the measures on the different occasions or times divided by the pooled standard deviation can be converted to an effect-size. For example, consider the plethora of research on the quality of teaching at the university level as evaluated by student ratings. Typically these studies relate some ratings by students to subsequent student assignment/end of the year assessments, and this correlation can be converted to an effect-size. The individual effect-sizes from each comparison within a study can be accumulated across the many studies to form an overall view of the effects of teacher evaluations on student outcomes. Studies that undertake such accumulations are called ‘meta-analyses’ (Cooper & Hedges, 1994; Glass, McGaw, & Smith, 1981). There have been five meta-analyses based on over 141 studies on this topic (see Table 8.1). The average effect across all these five meta-analyses is .44, and it can thence be concluded that student rating of the quality of teachers and teaching is reasonably Table 8.1 Five meta-analyses relating evaluation of teachers with student achievement Author

Year

No. studies

No. effects

ES

Variable

Cohen

1980

22

22

0.33

Cohen

1981

19

19

0.68

Cohen

1981

41

58

0.48

Abrami, Leventhal, & Perry Cohen

1982

12

12

0.28

1981

47

74

0.44

Feedback from student ratings Student rating of teacher Student rating of teacher Expressiveness of teacher Student rating of teacher

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related to learning outcomes. This overall figure, while interesting, can then be evaluated for specific contributions. For example, Cohen (1980) used 22 studies and found that there was an effect-size of .33 between those student evaluations that also included and did not include feedback to the teacher. This means that by including student-rating feedback raises the quality of teaching by about one-third of a standard deviation and this seems worthy from implementing such feedback. Because meta-analyses include many studies it is often possible to make conclusions that are precluded by a single or smaller set of studies. For example, across these 5 meta-analyses, it was found that the ratings with achievement were higher for teaching skill and teachers’ knowledge of the subject than with teachers’ rapport with students and the difficulty of the course.

800 þ Meta-Analyses Relating to Student Achievement The evidence for the claims in this chapter comes from a synthesis of over 800 metaanalyses (Hattie, 2009a). The aim of the synthesis is to place the various influences on student achievement along an underlying achievement continuum. The range of “achievement” covers a wide range of outcomes —final examinations, standardized tests of content knowledge, lab reports etc. The scale of this continuum is effect-sizes, which as noted above, is a common expression of the magnitude of study outcomes for many types of outcome variables. An effect size of d ¼ 1.0, for example, indicates an increase of one standard deviation on the outcome, and is typically associated with advancing student’s achievement by two to three years, improving the rate of learning by 50%, or a correlation between some influence and achievement of approximately r ¼ 0.50. When implementing a new program, an effect size of 1.0 would mean that, on average, students receiving that treatment would exceed 84% of students not receiving that treatment. An effect-size less than .2 can be considered small, .4 average, and greater than .6 large (although care is needed given that there may be important moderators and mediators of any overall effect-sizes; for more details on the methodology of this study, see Hattie, 2009a). It was possible to locate over 800 meta-analyses, which encompassed 52,637 studies, 146,142 effect sizes, and based on about 240m students. These studies related to the influence of some program, policy, or innovation on academic achievement in school (early childhood, elementary, high, and tertiary). While it is the case that most are derived from the compulsory sector, there are still many from the tertiary sector, and the good news is that the underlying messages underlying successful innovations are quite similar across the sectors. Figure 8.1 shows the overall distribution of all the effect sizes from each of the 800 þ metaanalyses. The y-axis represents the number of effects in each category, while the x-axis gives the magnitude of effect sizes. A major conclusion from this Figure is the confirmation of the idea noted earlier in this chapter—that “everything works.” Ninety-five percent of all effect

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70

Number of effects based on 800+ meta-analyses

60

50

40

30

20

10

–4

–5

–6

9t

o–

5 t 61 o– 5 t 51 o– –3 5 t 41 o –2 –3 5t 1 o –1 –2 5t 1 o –0 –1 5t 1 o– .05 01 to .15 .09 to .25 .19 to .35 .29 to .45 .39 to .55 .49 to .65 .59 to .75 .69 to .85 .79 to .95 .89 1.0 t o 5 t .99 1.1 o 1.0 5t 9 1.2 o 1. 5 t 19 1.3 o 1. 5 t 29 1.4 o 1. 5 t 39 o 1.5 1.4 5t 9 1.6 o 1. 5 t 59 1.8 o 1. 0 t 69 o1 .89 2.0 0+

0

Effect-size

Figure 8.1 Distribution of effect sizes across all meta-analyses. Source: Hattie, J. A. C. (2009b). The black box of tertiary assessment: An impending revolution. In L. H. Meyer, S. Davidson, H. Anderson, R. Fletcher, P. M. Johnston, & M. Rees, Tertiary assessment and higher education student outcomes: Policy, practice, and research (pp. 259–276). Wellington, New Zealand: Ako Aotearoa.

sizes in education are positive. When teachers claim that they are having a positive effect on achievement or when there is a claim that a particular policy improves achievement this is almost a trivial claim: virtually everything works. Setting the bar of “what works best” in teaching and learning at the tertiary level should not be set at zero—that is any evidence of enhanced achievement is a poor starting point; given almost everything we do enhances achievement. No wonder every teacher can claim that they are making a difference; no wonder we can find many answers as to how to enhance achievement; no wonder every student improves. Raising achievement beyond an effect size of d ¼ 0.0 is so low a bar as to be dangerous and is most certainly misleading. Instead we should be aiming to enhance achievement at least to an average level—that is, an effect-size of .40; and this is the bench mark that is used to identify the very best strategies from the below average strategies. The d ¼ .40 is the hinge-point around which debate needs to occur in tertiary programs as to what is and is not effective.

Three Claims for Higher Education An examination of the strategies that meet or exceed this d ¼ .40 threshold reveals three emergent themes, or premises upon which to build a model of effective

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tertiary teaching and learning. The purpose of these three premises is to make student learning “visible” to the academic and this is the key to successful teaching. The three claims—and accompanying recommendations to instructors—relate to intentions, strategies, and feedback, and together they maximize achievement outcomes.

The Best Teachers Communicate Clear Learning Intentions and Criteria for Success Learning intentions describe what it is we want students to learn in terms of the skills, knowledge, attitudes, and values within any particular unit or lesson. Learning intentions need to be clear, and provide guidance to the teacher about what to teach, help students become aware of what they should learn from the classes, form the basis for assessing what the students have learnt, and for assessing what the teachers have taught well to each student. Learning intentions without clear success criteria are of little value. The purpose of success criteria is to make students understand what the lecturer is using as the criteria for judging their work, and, of course, to ensure that the lecturer is clear about the criteria that will determine if the learning intentions have been successfully achieved. Given the absence of specific success criteria, then the major source of information to students is in their interpretations of what is required by the assessments. It should be no wonder students place so much reliance on assessments as so often these are often the only tangible evidence of success criteria in many courses. Further, if the success criteria are not clear there is a tendency to over rely on surface (more the content) rather than deep or conceptual understanding as this increases the probability of coverage of what is asked, particularly in closed examinations. A good illustration of combining learning intentions and success criteria is mastery learning and in particular Keller’s personalized system of instruction (PSI), often used in tertiary settings (indeed more so than in compulsory schooling years). Developed in the 1960s, the personalized system employs a highly structured, student-centered approach to course design that emphasizes self-pacing and mastery (Keller, 1968; Keller & Sherman, 1974). The key features are that the learning intentions are stipulated at the start of the program and students proceed through the course at their own pace and must demonstrate mastery of each component of the course before proceeding to the next. Teachers need to provide appropriate and targeted teaching materials and be involved more in tutorial support and in providing motivation for students to complete the work and attain the goals. There have been 12 meta-analyses of mastery learning and each has shown that students using these methods had higher grades and higher satisfaction rates than students in conventional classes (and that study time was similar in both types of classes). These meta-analyses are based on 263 studies, with an average effect-size of .53, which is well above the .4 hinge-point.

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The difficulty or degree of challenge of these learning intentions or goals set by and for students is critical. There are 11 meta-analyses relative to the difficulty of goals and the overall effect size is a large d ¼ 0.67. The performances of the students who have the most challenging goals are over 250 percent higher than the performance of subjects with the easiest goals (Locke & Latham, 1990). A major reason challenging goals are more effective is that they lead to a clearer notion of success and direct the student’s attention to relevant behaviors or outcomes, whereas “doing your best” can fit with a very wide range of goals. These goals can be of many types—such as mastery or performance goals. With a mastery goal, students are oriented toward developing new skills, trying to understand their work, improving their level of competence, or achieving a sense of mastery based on self-referenced standards. Performance goals, however, involve students being more concerned with their performance relative to others, and completing courses for extrinsic reasons. As well as making them appropriately challenging, it helps if the student has a commitment and a sense of agency in attaining these goals. Having an intention to and knowing how to implement the mastery goals can be powerful. Gollwitzer and Sheeran (2006) completed a meta-analysis testing the notion that such implementation intentions help teachers and students attain goals. They used 63 studies and the effect size was d ¼ 0.65. It is not just the presence of a learning intention, and having commitment that helps, but most importantly it is having a sense of “if-then” that helps the implementation of goal intentions. Thus, a key component of successful teaching in higher education is setting appropriately challenging goals, developing commitment to attaining them, and developing intentions to implement strategies to attain them.

The Best Teachers Use Multiple Teaching Strategies that Emphasize Student Perspectives in Learning Newell (1990) noted that there are two layers of problem solving: applying a strategy to a problem, and selecting and monitoring that strategy. Such “thinking about thinking,” meta-cognition, or some degree of active control over the cognitive processes engaged in learning (such as planning how to approach a given learning task, evaluating progress, and monitoring comprehension) is a powerful ingredient in successful learning. It is the development of such selfregulation strategies that lead not only to successful academic outcomes but also enhanced engagement and satisfaction in higher education. The best teachers use multiple strategies to enhance these self-regulations skills, and provide assessment tasks aligned with the curricula intentions that allow for students to provide evidence of their “thinking about thinking.”

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In the higher education sector, Lavery (2008) found a large (d ¼ 0.46) effect on achievement from meta-cognitive study skills interventions. She found the highest effects from strategies that aimed at the “forethought” phase of learning (i.e., before the learner engages in the task such as goal-setting and planning), as well as self-instruction, and self-evaluation. The highest ranked strategy was organizing and transforming (such as summarizing and paraphrasing), particularly when these activities promoted a more active approach to learning tasks. The more passive and less active involvement strategies were ranked lowest (such as record keeping, imagery, time management, and restructuring the learning environment). It is worth noting that there were some initial decrements in performance as students were taught some of these more active strategies, suggesting that a longer time for implementing them was necessary, possibly due to a need for some un-learning of prior study skills before new learning strategies can be used successfully (Lavery, 2008; Nist & Simpson, 1989). Hattie, Biggs, and Purdie (1996) found that many lower level study skills (e.g., mnemonics, rote memory) can be taught via generic study skills courses but the deeper level study skills (e.g., meta-cognitive skills) needed to be taught within the content of the course. The student needs to know various strategies that are appropriate to the task at hand: the how, when, where, and why of their use. Further, the effect of teaching study skills to students at the college level who are struggling is quite low as it is the content that they are probably not mastering not the meta-cognitive strategies of thinking about the content (Burley, 1994; Kulik, Kulik, & Shwalb, 1983). A more effective strategy may be to provide the students with the instructors notes to add to during lectures, which was much more effective than students creating their own (Kobyashyi, 2005, d ¼ 0.82). This emphasis on utilizing multiple teaching and learning strategies should not be confused with the notions of adapting to students learning “styles,” as there is no support for the popular notions that teaching should be matched to students “styles of learning.” The typical claim is that students with dominant learning styles, such as auditory, visual, tactile, or kinesthetic styles showed greater academic gains as a result of congruent instructional interventions than those students who had mixed preferences or moderate preferences. The scales of these styles are often inconsistent, the relations between the scales and teacher judgments very low, and the evidence damning of the claims. Coffield, Moseley, Ecclestone, and Hall (2004) completed an extensive analysis of various learning style models, and found few studies that met their minimum acceptability criteria, and they provided many criticisms of the field such as: too much overstatement, poor items and assessments, low validity and negligible impact on practice, and much of the advocacy in this is aimed at commercial ends. They found much support for developing student learning strategies, but not for considering learning styles.

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The Best Teachers Seek Feedback Regarding the Effectiveness of their Teaching and Provide Feedback to Students Regarding the Effectiveness of their Learning Providing feedback to students is among the most powerful enablers for their learning and, as critical, is when teachers seek feedback about the impact of their teaching. The latter is not only powerful for the teacher to thence better understand the impact of their teaching but for subsequent effects on students. Feedback is any information provided by an agent (e.g., instructor, peer, book, or one’s own experience) about aspects of one’s performance or understanding. For example, an instructor can provide corrective information, a peer can provide an alternative strategy, a book can provide information to clarify ideas, a parent can provide encouragement, and a learner can look up the answer to evaluate the correctness of a response (Hattie & Timperley, 2006). Similarly, a teacher can receive feedback from how students answer assignments, the nature of the questions they ask in class (which can be more powerful than the answers to the teachers’ questions), and by noting how much effort and commitment they put into the course. Two cautions. First, the incidence of feedback received by students is minimal (and this is not to be confused from the amount of feedback teachers believe they deliver—it must be received to be effective, and rarely is group feedback seen as pertinent to each student). Carless (2006) asked students and tertiary instructors whether instructors provided detailed feedback that helped students improve their next assignments. About 70 percent of the instructors claimed they provided such detailed feedback often or always, but only 45 percent of students agreed with their teachers’ claims. Further, Nuthall (2005) found that most feedback that students obtained was from other students, and most of this feedback was incorrect. A second caution is that not all feedback is powerful as there is much variability in the effect-sizes from the many meta-analyses. Programmed instruction, praise, punishment, and extrinsic rewards are among the least effective forms of feedback for enhancing achievement. Providing feedback is not about giving rewards, but rather providing information about the task. Feedback is more effective when it provides information on correct rather than incorrect responses, when it builds on changes from previous trails, when it is perceived as a low rather than high level of threat to self-esteem (presumably because low threat conditions allow attention to be paid to the feedback), and when goals are specific and challenging but when task complexity is low (Kluger & DeNisi, 1996). Hattie and Timperley (2006) developed a model for effective feedback whereby they argued that the main purpose of feedback is to reduce discrepancies between current student understandings and the learning intentions and success criteria. The strategies that students and teachers use to reduce this discrepancy depends partly on the level at which the feedback operates. These levels include the level of task performance (the content), the level of process of understanding how to do a

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task (or think about the content), the regulatory or meta-cognitive process level (monitoring the learning), and the self or person (unrelated to the specifics of the task). Feedback has differing effects across these levels. The effects are highest when the purpose of learning is matched to the feedback information—when first learning content then feedback is most efficacious at the task level (e.g., correct or incorrect); when thinking and deeply understanding this content then feedback is most efficacious at the process level (e.g., providing different ways of considering the material, counter arguments); when the material is understood and conceptually related to other materials then feedback is most efficacious at the regulation level (e.g., monitoring error detection, building confidence to take stances/ arguments about the material). Feedback about the self is rarely effective at any stage with respect to achievement gains, although self-feedback may assist in keeping on-task, building confidence, and maintaining engagement but should never be confused with mastering the content or understanding. The major feedback questions are “Where am I going?” (learning intentions/ goals/success criteria), “How am I going?” (self-assessment and self-evaluation), and “Where to next?” (progression, new goals). An ideal learning environment or experience is when both teachers and students seek answers to each of these questions. These three questions do not work in isolation at each of the four levels, but typically work together. Feedback relating to “How am I going?” has the power to lead to doing further tasks or “Where to next?” and “Where am I going?” As Sadler (1989) has convincingly argued, it is closing the gap between where the student is and where they are aiming to be which leads to the power of feedback. Oh, how powerful it is when current status and learning success is known as then the gap is more apparent. One of the more fascinating developments in the compulsory sector has been the use of formative assessment. Often termed assessment for learning (AfL, to juxtapose with assessment of learning and assessment as learning), it involves using assessment to enhance the teaching of the courses as much as using it to assist students to understand what they do and do not know and understand—during the course. While these notions are becoming well used in many elementary and high schools, the revolution of AfL is beginning to be encountered in higher education classes. The advent of (“clickers” or audience response systems, Bruff, 2009) is part of this beginning where teachers are receiving feedback during their lectures about what the students do and do not understand thus providing opportunities for repeated or revised teaching. Other advances include the revolution of computerized essay scoring, computerized peer critique, and computerized interactive tasks. Success comes from feedback from these methods to the teacher about what they taught well, gaps, strengths, to whom in the class, and how their learning intentions have been realized. This requires a change of mindset from thinking assessments are for and about the students, but how they can contribute more directly to the quality and nature of teaching.

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To enact the principles outlined in the chapter will entail a major change in higher education classrooms. The methods of teaching from the past two centuries will need to undergo major changes. In a recent review of tertiary assessment I concluded (Hattie, 2009b): Alexander Graeme Bell invented the telephone from a tool he made for teaching the deaf. If he saw the telephone in 1990 he would still recognize it. If he saw it now in 2008 he would not immediately see the connection between what he invented and Skype, internet, faxing, texting, virtual marts, i-tunes, etc. There will be nothing familiar to Bell. A similar punctuated equilibrium is close in tertiary assessment. Nineteenth century professors would see many similarities between their assessments and today’s university assessment. Over the next decade we will witness the greatest revolution in the role of assessment in tertiary education—it will move from a device to sum up what we think students need to know, to providing feedback into the teaching and learning cycle; it will involve more than surface and occasionally deeper knowledge and understanding; it will involve peer assessment and computerized scoring; it will involve aspects of Second Life and interactivity; it will see more use of computerized adaptive testing; and the quality of these assessments will be set higher, the qualities will be more public, and students will be the major beneficiaries of this revolution. The revolution will encompass “feedback from assessment” and the development of visible learning and visible teaching.

Conclusions This chapter has aimed to show that there is already a tremendous amount of research on effective teaching and learning, and such research is flourishing in the tertiary sector. From a synthesis of over 800 meta-analyses it is possible to locate the most important factors in the “mindsets” of the teachers, and they relate to the explicitness and challenge of their intentions and notions of success, in teachers’ use of multiple teaching strategies, and the manner in which they use and seek feedback about their effectiveness. The best teachers have high levels of realization about the ways in students learn using various strategies and from each other, and are most transparent about what they value in outcomes (at the surface, deep and conceptual levels, and evidenced in what they teach and what they assess). There is little to be gained by considering higher education teaching as delivering knowledge or learners as receptacles of fascinating facts, but instead what matters most are the transparency of the challenges and outcomes of the course, the use of multiple teaching strategies that emphasize student problem solving and engagement with the content, and the seeking by teachers of feedback about the impact of their teaching. A supportive higher education culture could well move past esteeming teaching or research, and esteem the findings from research on teaching. This, surely, is the

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point of this book. A recent survey of 142 academics in psychology departments found that about half claimed that their department encouraged SoTL activities, about third of their colleagues were not involved in SoTL activities, and the conclusion was that while there may be support and policies there is still much translation into practice needed (Gurung, Ansburg, Alexander, Lawrence, & Johnson, 2008). That there continues to be a close to zero relationship between research productivity and teaching evaluations (Hattie & Marsh, 1996; Marsh & Hattie, 2002) means that there is still much to do in the higher education sector. It is necessary that more research on teaching is introduced into teaching. Such a supportive research and teaching environment is needed, and this takes sustained and deliberate actions on the part of those most senior in higher education to encourage and esteem the scholarships of teaching. What seems needed are teaching methods that allow teachers to know more about student perspectives in learning, to communicate clear learning intentions and criteria for success, to optimize meta-cognitive and student-regulated learning, to increase feedback that is appropriately received by students, and most critically for teachers to seek greater feedback from students to them then lead to changes in their teaching. The major underlying basis of the model outlined in this chapter is that when student learning becomes “visible” to the teacher this greatly enhances the probability of student achievement. The core ingredients in this visibility is making learning intentions and success criteria transparent, engaging students in the challenge of learning, moving them from surface to deep to conceptual processing, and most of all moving the priority in assessment from a purpose to evaluate the outcomes of learning to using assessment as part of learning and teaching.

References Abrami, P. C., Leventhal, L., & Perry, R. P. (1982). Educational seduction. Review of Educational Research, 52(3), 446–464. Boyer, E. L. (1990). Scholarship reconsidered: Priorities of the professoriate. Princeton, NJ: Princeton University Press, The Carnegie Foundation for the Advancement of Teaching. Bruff, D. (2009). Teaching with classroom response systems: Creating active learning environments. San Francisco: Jossey-Bass. Burley, H. E. (1994, April). A meta-analysis of the effects of developmental studies programs on college student achievement, attitude, and persistence. Paper presented at the Annual Meeting of the American Educational Research Association, New Orleans, LA. Carless, D. (2006). Differing perceptions in the feedback process. Studies in Higher Education, 31(2), 219–233.

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Coffield, F., Moseley, D. V. M., Ecclestone, K., & Hall, E. (2004). Learning styles and pedagogy: A systematic and critical review. London: Learning and Skills Research Council. Cohen, P. A. (1980). Effectiveness of student-rating feedback for improving college instruction: A meta-analysis of findings. Research in Higher Education, 13 (4), 321–341. Cohen, P. A. (1981). Student ratings of instruction and student achievement: A metaanalysis of multisection validity studies. Review of Educational Research, 51(3), 281–309. Cooper, H. M., & Hedges, L. (1994). The handbook of research synthesis. New York: Russell Sage Foundation. Glass, G. V., McGaw, B., & Smith, M. L. (1981). Meta-analysis in social research. Beverly Hills: Sage. Gollwitzer, P. M., & Sheeran, P. (2006). Implementation intentions and goal achievement: A meta-analysis of effects and processes. Advances in Experimental Social Psychology, 38, 69–119. Gurung, R. A. R., Ansburg, P. I., Alexander, P. A., Lawrence, N. K., & Johnson, D. E. (2008). Scholarship of teaching and learning strategies and tactics. Teaching of Psychology, 35, 249–261. Hattie, J. A. C. (2009a). Visible learning: A synthesis of 800 meta-analyses relating to achievement. Oxford: Routledge. Hattie, J. A. C. (2009b). The black box of tertiary assessment: An impending revolution. In L. H. Meyer, S. Davidson, H. Anderson, R. Fletcher, P. M. Johnston, & M. Rees (Eds.), Tertiary assessment and higher education student outcomes: Policy, practice, and research (pp. 259–276). Wellington, New Zealand: Ako Aotearoa. Hattie, J. A. C., Biggs, J., & Purdie, N. (1996). Effects of learning skills interventions on student learning: A meta-analysis. Review of Educational Research, 66(2), 99–136. Hattie, J, & Marsh, H. W. (1996). The relationship between research and teaching—a metaanalysis. Review of Educational Research, 66, 507–542. Hattie, J. A. C., & Timperley, H. (2006). The power of feedback. Review of Educational Research, 77(1), 81–112. Keller, F. S. (1968). “Good-bye, teacher. . .” Journal of Applied Behavior Analysis 1(1), 79–89. Keller, F. S., & Sherman, J. G. (1974). PSI, the Keller Plan handbook: Essays on a personalized system of instruction. Menlo Park, CA: Benjamin. Kluger, A. N., & DeNisi, A. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254. Kobayashi, K (2005). What limits the encoding effect of note-taking? A meta-analytic examination. Contemporary Educational Psychology, 30, 242–262. Kulik, C.-L. C., Kulik, J. A., & Shwalb, B. J. (1983). College programs for high-risk and disadvantaged students: A meta-analysis of findings. Review of Educational Research, 53(3), 397–414.

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Lavery, L. (2008). Self-regulated learning for academic success: An evaluation of instructional techniques. Unpublished PhD, The University of Auckland, Auckland. Locke, E. A., & Latham, G. P. (1990). A theory of goal setting and task performance. Englewood Cliffs, NJ: Prentice Hall. Marsh, H. W., & Hattie, J. (2002). The relation between research productivity and teaching effectiveness: Complementary, antagonistic, or independent constructs? Journal of Higher Education, 73(5), 603–641. Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press. Nist, S. L., & Simpson, M. L. (1989). PLAE, a validated study strategy. Journal of Reading, 33(3), 182–186. Nuthall, G. (2005). The cultural myths and realities of classroom teaching and learning: A personal journey. Teachers College Record, 107(5), 895–934. Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional Science, 18(2), 119–144. Tomcho, T. J., & Foels, R. (2008). Assessing effective teaching of psychology: A metaanalytic integration of learning outcomes. Teaching of Psychology, 35, 286–296.

9 Understanding Faculty Reluctance to Assess Teaching and Learning A Social Psychological Perspective Dana S. Dunn, Maureen A. McCarthy, Suzanne C. Baker, Jane S. Halonen, and Stacy Boyer

Reactance, as a motivational state, has two direct effects: It impels attempts to regain lost or threatened freedoms, and it magnifies motivation toward the threatened or lost behaviors and/or their intended outcomes, making them subjectively more attractive. (Brehm & Brehm, 1981 p. 116)

Autonomy is singularly important to members of the professoriate. Faculty members generally place great value on what they do and on the time they spending doing it, whether the activity is teaching, scholarship and research, or service. Consider Figure 9.1, an illustration of how professors spend their time that originally appeared in the online academic comic strip, “Piled Higher and Deeper.” The strong desire for autonomy is captured (if only partially tonguein-cheek) by the right side of Figure 9.1, which reveals the crux of the matter: Professors resent being told what to do and very much value their professional independence. Academic freedom—the latitude to teach and profess on intellectual issues, especially controversial ones—is an honored tradition in American higher education, one that is not only rarely questioned by administrations, boards of trustees, or other interested stakeholders, but that often is explicitly protected in institutional mission statements and policies. In the past decade or so, the call for educational assessment—to establish what and how well students are learning—has been perceived by many faculty members to be a hindrance, if not an outright threat, to their sovereignty in the classroom

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(e.g., Halpern, 2004; see also, Aleamoni, 1987). Such reactions—we characterize them as misunderstandings—represent a common occurrence and challenge on many campuses. Our goal in this chapter is to treat faculty reluctance to embrace assessment as an opportunity to apply social psychological theory to the teaching side of student learning. Although assessment is now an integral part of quality teaching and learning at the undergraduate level (e.g., Dunn, McCarthy, Baker, Halonen, & Hill, 2007; Halpern, 2004; Shavelson & Huang, 2003), it is by no means universally embraced. In this chapter, we are specifically concerned with understanding and constructively changing faculty attitudes toward assessment from a negative or neutral valence to one that is positive. To do so, we rely on a framework provided by reactance theory (Brehm, 1966; Brehm & Brehm, 1981; see also Wright, Greenberg, & Brehm, 2004), which posits that real or perceived threats to people’s freedom trigger actions aimed at restoring that freedom (see also, de Charms, 1968). In the case of requiring routine assessment activities, administrations and program or department chairs are seen as “imposing” demands upon instructors, which can have aversive emotional consequences (e.g., anger, anxiety, annoyance, bitterness, indignation, and resentment) for faculty members. Behaviorally, reactance often manifests itself in individuals as a marked tendency to do the opposite of what they are told to do; teaching faculty, for example, may refuse to administer assessment tools, “forget” to do so, challenge their utility or veracity, or be otherwise contrarian. In short, faculty members dislike being told what to do or how to do it in their classrooms. What can be done to ameliorate this situation? We begin by discussing the social psychology of assessment and then explore how psychological reactance can serve as a helpful framework for characterizing

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faculty attitudes and behaviors towards assessment. We then discuss ways that individuals who are responsible for promoting and implementing assessment activities (chiefly, but not exclusively, department chairs, program heads, assessment directors) can constructively persuade their reticent colleagues to consider the actual need for and virtues of academic assessment. Finally, we close with an appeal to faculty colleagues to avoid demonstrating self-serving biases regarding their teaching effectiveness and look to future possibilities for further developing a social psychology of assessment.

Assessment, Evaluation Apprehension, and Social Psychology Assessment is a global term used to refer to the authentic evaluation of teaching and learning outcomes (e.g., Angelo, 1999; Mentkowski et al., 2000) that involves identifying, adapting, or developing instruments and assignments that measure how faculty members actually teach and how students really learn (e.g., Maki, 2001; Zubizarreta, 2004). By collecting and analyzing assessment data, educators hope to demonstrate that students are learning what their teachers expect them to learn. Educators then use assessment results to make needed changes to improve teaching and learning in a given course or within a curriculum (e.g., Dunn, et al., 2007; Dunn, Mehrotra, & Halonen, 2004), as well as to address accreditation issues (Stanny & Halonen, 2011). Assessment activities matter because they provide solid, persuasive evidence that teaching is effective, demonstrable, and accountable. How and why is social psychology related to assessment efforts? Allport (1985) classically defined social psychology as people being influenced by the actual, implied, or imagined presence of others. This definition readily characterizes faculty members’ concerns regarding having their teaching activities and responsibilities unduly monitored, even scrutinized, by others (e.g., department chairs, deans). Inevitably, assessment initiatives trigger evaluation apprehension or anxiety regarding whether and how peers or supervisors will use assessment findings for faculty performance reviews, which in turn may have tangible (e.g., salary) and intangible (e.g., status in department) outcomes.

Evaluation Apprehension as Faculty Response to Assessment Although campuses and disciplines have shown increasing acceptance of assessment initiatives, faculty members often remain reluctant to accept or incorporate assessment activities into their teaching efforts. Perhaps the chief concern for faculty is how any evidence from various classroom and teaching assessments will be used in evaluations of their performance (e.g., Clarke, Ellett, Bateman, &

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Rugutt, 1996; Dunn, McCarthy, Baker, & Halonen, in press). This form of evaluation apprehension is rooted in a concern that assessment data will be used in a summative manner. Summative assessment refers to the use of findings to reward good performance (“good teaching”) in the classroom and to punish counterproductive or poor performance (“bad teaching”). Concerns regarding compensation, teaching load, course releases and reductions, sabbatical leaves, research resources, and, of course, promotion and tenure, are all implicated. Thus, faculty concern regarding summative functions of assessment is reasonably justified (e.g., Dunn, et al., in press; Dunn, et al., 2007; Halpern, 2004). Instead, the primary objective of assessment should be formative, focusing on what is done well or those areas of apparent quality or educational success, as well as to correct or reshape areas that need improvement. How faculty perform directly influences student engagement and learning (e.g., Kuh & Hu, 2001; Lundberg & Schreiner, 2004; Umbach & Wawrzynski, 2005). Formative assessment uses the results of assessment data (e.g., curriculum review, course evaluations, learning outcome measures, syllabus surveys, major field/area tests, exit interviews) to reform, revise, update, or simply improve educational practices in a given class. The goal of high quality formative assessment is to acknowledge what is already being done well, simultaneously identifying areas where opportunities for continued growth and development exist. Moreover, formative assessment does not have a definitive end; refinement and revision of teaching and learning methods is presumed to be continual, whereas summative assessment has end point consequences (i.e., reward or punishment). We hasten to add that many faculty members, similar to many departments found at various colleges and universities, do welcome assessment activities. On some campuses, assessment can even be said to be a routine part of the educational experience and institutional mission (see, for example, Stoloff, Apple, Barron, Reis-Bergan, & Sundre, 2004). However, our goal in this chapter is to examine faculty reluctance to embrace assessment and to suggest ways to deal constructively with it from a social psychological standpoint.

Faculty Freedom Threatened? Psychological Reactance and Establishing Assessment Practices Reactance theory is one of the most well-established theories in social psychology. Brehm (1966) developed reactance theory as a framework for explaining why individuals sometimes rebel when external forces impose behavioral restrictions or social pressure upon them. Generally speaking, people view their own freedom to act (whether real or perceived) to be a beneficial state and they perceive restrictions on that freedom as an unfavorable condition. A person’s level of reactance varies according to how much freedom is restricted; the less freedom,

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the more reactance, with the elimination of the former culminating in the highest level of the latter. In the context of faculty life, the term “freedom” involves an individual’s opportunity to pursue one course of action over others, as well as the opportunity to preserve behavioral options—ideally as many as possible—for some future time. When faculty members believe that their administrators threaten some freedom, they may act to maintain or regain it (e.g., Tjosvold, 1978). For example, choosing what course will be taught in a given semester—as well as at what time and on what day—is a privilege that faculty may construe as a right guaranteed by the principles of academic freedom (e.g., AAUP, 1940). If administrators reduce the latitude faculty exercise about their preferred schedules, a common response would be to rally colleagues around the issue and to lodge a collective complaint with a department chairperson or even a dean about the lost or threatened privilege. Not surprisingly, many administrators rely on the good will of faculty members to complete the necessary tasks associated with running a department or program, as well as meet the various obligations that allow the larger institution to run smoothly (e.g., committees, task forces). Thus, within academic settings, faculty reactance can be successful at slowing or impeding change even if administrators have designed the changes to benefit students, reallocate resources, or reduce expenses. Reactance theory also implies a desire or need for control on the part of professors: To wit, faculty members like to be in charge of all aspects of their classroom, including determining readings on syllabi, designing assignments and learning activities, writing and delivering lectures, and assigning final grades. Understandably, they may resent outside interference, no matter how wellintended or, in some cases, how mandated (via boards of regents or trustees, or even their own faculty unions) (cf., Deutsch & Krauss, 1962; Tjosvold, 1977, 1978). To a certain degree, reactance is also an individual difference variable: Its magnitude will vary according to the situation and the person or people involved. The more important the freedom that is restricted or lost, the greater the degree of reactance that is likely to occur as a result (Brehm, 1966). Thus, a faculty member who is routinely accustomed to integrating new department initiatives into the fabric of her courses will be less upset by nascent changes related to assessment than a colleague who has taught for decades in relative isolation from administrative oversight.

Main Consequences of Psychological Reactance When conditions trigger reactance and produce emotional responses, three primary consequences are likely to transpire: anger, upset, and concern (Brehm, 1966; Brehm & Brehm, 1981). First, any lost or threatened freedom suddenly becomes more attractive to the affected individual. The change in real or

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perceived freedom is an important issue, as any change to faculty members’ expectations can be perceived as a threat (i.e., a potential loss of freedom). This observation may seem strange, as common sense should presumably guide people’s responses except in situations of real or serious threat. Nonetheless, research on reactance theory demonstrates that even minor changes to choice options can elicit a response (Brehm, 1966; Brehm & Brehm, 1981). Even when some option is not especially valued, when it is suddenly no longer available, people suddenly place much greater value on that option than before. Thus, even the loss of an under-used or ignored resource, such as an abandoned office or an out-dated piece of equipment, can trigger psychological reactance. Any loss of individual or departmental “turf” is apt to have negative repercussions for various agents, including the department or program chair, as well as the administration; this, too, is a form of reactance. A second consequence of reactance is behavioral: Individuals attempt to regain the lost or threatened freedom. When assessment represents a threat, faculty members may assert their teaching or classroom autonomy by questioning the need for or introduction of an assessment program during a department or general college faculty meeting. Debate may occur in such sanctioned settings, and may also continue for some time in the public and private spaces that faculty inhabit. Typical faculty reactions include questioning the veracity, validity, or even the necessity of the assessment (e.g., Halpern, 2004). Resistant faculty may argue that such imposed activities will disrupt classes, perhaps even making more work for students or disrupting the coverage of essential material. The most resistant faculty members may even suggest that such interventions constitute a threat to their academic freedom. The source of faculty resistance is apt to be linked to the fact that someone higher in the institutional hierarchy—a chairperson, a dean, or some other administrator—can be implicated as the causal agent. Reactance can certainly occur in superior–subordinate relationships, even when faculty members enjoy a fair amount of autonomy and professional latitude. Indeed, Tjosvold (1978) suggested that school administrators who invoke control risk evoking reactance in teachers. A common source of such conflict is the fact that administrators and teaching faculty may have opposing views (and perspectives) about educational goals and the procedures needed to achieve them. Of course, beyond an emotional response coupled with counter-arguments, what else is apt to happen? Some faculty members may refuse to include assessment activities in their courses. Thus, despite administrative pleas, carrot and stick appeals, or even fiats, some colleagues may continue to do exactly what they did before (i.e., to avoid implementing assessment practices by neglecting them outright or by “forgetting” to collect relevant data, administer appropriate measures, and so on). In effect, the faculty members roll the dice that the administrator will not exact any negative consequences for failing to comply with

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assessment “requirements.” As such, the calculated risk of opposing assessment may be worth taking. We will also point out that in some cases, the mandate for assessment does not come from an institution’s administration, but from an outside source, such as a board of trustees or an accrediting body. Administrators, then, may just be the proverbial messengers, and some of them may not necessarily view assessment as beneficial; they may regard it as “something that must be done.” Administrators are not immune from reactance, either. Possibly because of their own reactance, some administrators will tell faculty they have to do assessment, but provide no support or training for how to do it appropriately, never check to make sure it is done, never reward those colleagues who do it or provide any consequences for (i.e. “punish”) those who do not do it. Thus, it may not just be faculty reactance at play on a given campus. The third and final consequence of reactance reactions is aggression aimed at the person or persons inhibiting the real or perceived freedom. Fortunately, outright hostility is uncommon in academia but incivility may be on the rise (Twale & De Luca, 2008). Some faculty members may harbor aggressive thoughts and unfavorable opinions towards the individuals who are leading an assessment campaign for example (Miller, Lane, Deatrick, Young, & Potts, 2007). Thus, they may engage in derisive chatter with peers about administrative malfeasance behind closed doors, but rarely, if ever, engage in provocative or hostile acts. Most campuses express commitment to the principle of collegiality, or the capacity to work in a constructive, collaborative, and respectful fashion. Both sides of the faculty–administrative divide tend to perceive disruption of collegial practice to be problematic. No doubt many college or university administrations wonder whether continuing an emotionally charged campaign for assessment (or some other change) is worth the disruption and ill will it can create. As a result, administrators sometimes slow, alter, or even scale back assessment efforts so that faculty members can recover their lost autonomy. Once some semblance of freedom is restored, reactance dissipates. Interestingly, in general, how a freedom is restored does not seem to matter a great deal (see Brehm & Brehm, 1981). Individuals do not need to exercise personal control to reduce reactance; the sudden, unexpected return of a right is sufficient to quell the reactance response (e.g., Schwarz, 1980, cited in Brehm & Brehm, 1981). Thus, people do not necessarily need to take dramatic action to restore their own freedom for reactance to fade. The spontaneous return of a right, whether for political, circumstantial, or even quixotic reasons, may be sufficient to reduce a threat response.

Reactance As a Response to Academic Change All three consequences anticipated by reactance theory pose problems for introducing change in an academic program or department. Of particular concern is that any threats to freedom reduce the impact of social influence (i.e., how people

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try to change the attitudes or behaviors of others), a key prediction of reactance theory (Brehm & Brehm, 1981; Wicklund, 1974). In the present context, administrators and department chairs risk losing faculty cooperation and good will if faculty see assessment activities as being imposed or unduly onerous. Worse still, student learning may be less than optimal if faculty members’ resistance to assessment cannot be overcome by reasonable and rational means. In other words, faculty reactance to implementing assessment may lead them to miss opportunities to make teaching and learning activities more effective. In ideal situations, faculty members who understand the advantages of assessment programs will try to convince their more entrenched peers to be more open-minded. We believe that constructive persuasion based on sound social psychological principles can be used to overcome faculty reactance.

Constructive Persuasion: Maintaining Faculty Autonomy while Opening Minds to Assessment Before turning to matters of persuasion, we want to remind readers that reactance in academic contexts is normative; it happens on virtually every campus to some degree whenever some change is on the horizon. Indeed, its typicality should serve as a reminder that there is nothing inherently wrong when faculty (or members of almost any group) protest changes in their affairs, expectations, or degree of control. That being said, reactance can cloud faculty judgment so that they do not recognize the potential benefits associated with assessing teaching and learning. Thus, dealing with reactive faculty members requires reliance on tools associated with attitude change. Social psychological research on persuasion is extensive (e.g., Maio & Haddock, 2007; Petty, Wheeler, & Tormala, 2003) and its origins lie in literature on attitudes (for reviews, see Albarracin, Johnson, & Zanna, 2005; Crano & Prislin, 2008; Eagly & Chaiken, 1993). The goal of persuasion or attitude change is simple: To alter a person’s cognitions, feelings, and behaviors toward an object, idea, or individual from one direction (against) to another (in favor of). In general, change agents accomplish persuasion through the use of messages or arguments designed to encourage the intended audience to change their beliefs or evaluations. Careful study of how people process arguments reveals that intended messages alone do not lead to attitude change, rather, how and what people think about the messages matters. Specifically, what an individual thinks about a given message— his or her cognitive response—is of greater persuasive influence than the content per se (Maio & Olson, 1995). More favorable thoughts regarding a message will result in a greater likelihood for attitude change to occur than more unfavorable thoughts. Space constraints permit only a cursory review of how the theory and application of persuasion techniques can make assessment more palatable to

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faculty members. We discuss encouraging effortful information processing and then consider selected elements linked to persuasion: source variables, the message, context, and recipient variables. The overarching goal is not simply to convince faculty that assessment is a good idea and worthy of implementation, rather, persuasion should be used to promote logical arguments so that faculty may exercise their autonomy to accept and employ assessment tools.

Persuading Faculty by Engaging Effortful Processing Two dominant models of attitude change in contemporary social psychology are the elaboration likelihood model (Petty & Cacioppo, 1986) and the heuristicsystematic model (Chaiken, Liberman, & Eagly, 1989); both posit that motivated audience members capable of evaluating information relevant to a position advanced in a message (i.e., high elaboration) will do so by following what is referred to as the central or systematic route of persuasion. Faculty members often relish opportunities to analyze arguments, so presenting them with honest, if carefully crafted, evidence containing demonstrative data about the efficacy of assessment makes sense. In terms of their cognitive responses, the favorable or unfavorable reactions of audience members, as well as how confident they feel about the reactions, will decide how much, if any, attitude change occurs. Such presentations should be candid, not slick, and they should not be rushed. In fact, devoting a few meetings to presenting, considering, and discussing an assessment plan is apt to attract more faculty cooperation than a fast-tracked plan ever will (on some campuses, past battles over rushed decisions, great and small, rage on despite many examples of the failure of such an approach). Both the elaboration likelihood model and the heuristic-systematic model predict that an audience will engage in elaborate processing unless there is some compelling reason not to do so. When such reasons emerge, they will shift to the less elaborate route of processing so that simple positive or negative associations or heuristic processes (“I am always suspicious of administrators”) drive judgments. Research demonstrates that sustained attitude change usually results following effortful (central route) processing of persuasive messages rather than easier (peripheral route) processing. As we note later, however, on occasion peripheral routes to persuasion are useful tools, as when there is frequent collaboration or good will shared between administrators and faculty members. We now turn to variables than can promote or hinder persuasion (Hovland, Janis, & Kelley, 1953). We begin with source variables.

Source Variables: Who is Arguing for Assessment? Some speakers are simply more convincing than others. Source variables refer to a communicator’s qualities that promote or inhibit attitude change, which include

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attractiveness, credibility, expertise, and trustworthiness. Individuals who are high on any or all of these qualities are more likely to be able to persuade people to consider a messages position than those who are not. In an academic setting, who will be deemed a credible advocate for instituting assessment efforts? That depends. Sometimes an in-house expert can be convincing if he or she is respected by the relevant faculty group (e.g., department members, college faculty outside a department). If a colleague is well-known, as is often the case on small campuses, then his or her expertise may be trumped by preexisting affect (i.e., some colleagues like him, others don’t). Although he or she may be respected, a dean may be perceived as having an ulterior motive (e.g., carrying out a trustee-driven agenda), which means the chief academic officer may not always be persuasive. Due to the dean’s status, for example, compliant attitudes may appear on the surface, say, during a presentation on the benefits of assessment given in a faculty meeting, while the faculty members’ true attitudes remain resistant to implementing assessment activities (Kelman, 1958, 1974). One popular and sensible alternative is to bring an acknowledged and credentialed expert to campus. Ideally the individual will understand faculty concerns—for this reason, faculty members from other institutions who gradually gravitated to appreciating the advantages that good assessment practices offer are often good candidates. The guest expert should meet with faculty in small groups, perhaps run a workshop, and offer clear and compelling testimony, as well as concrete examples, about how assessment can be easily implemented while also enhancing teaching and learning. Experts can also provide strategies that may have practical appeal for faculty, such as techniques that save time or approaches that can lead to improved student evaluations.

Message Variables: Creating Reason-Based Rationales for Assessment A compelling speaker can only be so effective; the message being conveyed must also possess positive qualities that trigger listener interest. The characteristics of a persuasive message include its length, the sort of language used, the complexity of the arguments, their quality, and so on. By their nature, most faculty members are drawn to thoughtful presentations that, while engaging, also have depth. When an audience is motivated to think about the content of a talk or a document (e.g., an assessment plan for a department or college), then they may also be more open to persuasion, especially if the quality of the arguments is high. A caveat here is that if an audience feels at all pressured to accept a message, then they will respond negatively no matter how sound the supporting reasoning may be. Although there are conditions when fear appeals (e.g., “If we don’t put an assessment plan in place, then eventually some outside group—the state or even the government—may force us to accept one we won’t like”) work well, in general, they are usually not a good idea, as they can shut down thinking by creating defensive reactions. Such

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tactics also discourage faculty ownership and promote cyclical assessment practice (i.e., general inattention to assessment punctuated by bursts of activity just prior to the visit of any accrediting agency). One important consideration for structuring a message is attending to the match between the message and the recipient’s attitude. In other words, care must be exercised to craft a message that links up in some way to the existing attitude held by a target audience. Teachers are already likely to be open to entreaties focusing on improving teaching and learning; however, they may balk at the work involved in introducing assessment or raise other concerns. Happily, university faculty are probably more likely to be convinced by fact-based evidence concerning the virtues of assessment (e.g., how assessment can demonstrably enhance student learning while refining learning outcomes within a given course) than they are by more emotional appeals (e.g., “You will feel even better about your teaching efforts if you know what students are actually learning”). In essence, if processing is high and available arguments are compelling ones—that is, a good match exists—then persuasion can result.

Context Variables: How Assessment is Presented is As Important As What is Presented When administrators deliver persuasive messages, contextual issues matter. By context, attitude researchers refer to the mode of delivery for a message as well as the surrounding circumstances in which it is delivered. Where achieving attitude change is concerned, the how is as important as the what. Should an appeal regarding assessment activities be delivered verbally, say, in a public meeting? Or, is a message apt to have a better impact when it is delivered in written form, for example, in a memorandum or report from a dean, chair, or program director? Perhaps an article from the established assessment literature could be assigned as reading for those colleagues who sign up to attend a faculty development workshop. Our analysis of contextual conditions indicates that local campus conditions and traditions contribute to the success of assessment initiatives. On some campuses, faculty members resent being read to or lectured at in meetings. Instead, they prefer to prepare for a meeting by reading appropriate materials, which means those advocating assessment must prepare documents of a proper length and depth (while also attending to the potential impact of source and message characteristics). At other institutions, however, a leader—the college’s president, the dean or provost, or perhaps the head of the faculty senate, among other possibilities—leads all faculty discussions whether supporting documentation is provided or not. If this is the case, then source variables become a greater concern, as well. One critical contextual factor should not be ignored: Creating an appropriate positive, emotional association with a message by relying on a peripheral route to

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persuasion. Although it may seem trivial or perhaps too obvious a ploy, providing a positive experience at the same time persuasive communications are delivered or discussed is important as it generates positive mood (Hullett, 2005). Good food and drink are always a good idea. Thus, a pleasant luncheon, a cocktail or other social hour, or a dessert reception can go along way towards making the desired outcome and attitude change more palatable. Alternatively, providing faculty members with their own gratis copies of an assessment book or two can also indicate serous intentions as well as generate a positive atmosphere.

Recipient Variables: Working with the Faculty Recipient variables are qualities of the intended audience that have an impact on attitude change. There is one faculty characteristic that is arguably the most important recipient variable to consider: the need for cognition (Cacioppo & Petty, 1982). Some people simply enjoy thinking about things more than others and they intentionally seek out tasks, work, or activities that are intellectually engaging. Whether they are naturally drawn to mentally challenging work or their role demands it, most faculty members can be said to be relatively high in the need for cognition. Why does this matter? A high need for cognition has been shown to predispose recipients to process the content of a persuasive message carefully (even when the message is not personally relevant) and to base their subsequent attitudes on weight of the evidence presented. As might be surmised, those high in the need for cognition follow the central, rather than peripheral, route to persuasion. Organizers of meetings devoted to assessment presentations should heed this fact about their target audience and plan presentations accordingly. Thus, whenever possible, presentations should be detailed and data driven, and give and take discussion, even debate, between the speaker and audience should be facilitated. We encourage interested readers to delve more deeply into the available literatures on both reactance and persuasion, as we believe the theoretical frameworks nicely explain faculty reluctance and ways to convey the virtues assessment activities actually possess. We now offer suggestions aimed at making assessment a more attractive option and look ahead to future work on the social psychology of assessment.

A Social Psychology of Assessment Knowledge of reactance theory and the dynamics of attitude change can help individuals who are interested in bringing assessment activities to their campuses do so in ways that increase the odds of faculty acceptance. We have already outlined the ways in which faculty reactance can develop and hinder any chance for assessment reform, but what has gained little attention in the relevant literature

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are the problems that result from the failure to evaluate teaching and learning. The rejection of assessment tools due to the fear they will be used for summative ends has delayed research on the formative uses of assessment data. Ironically, perhaps, the result is an abundance of research concerning how evaluations may be used and improved upon for summative ends (e.g., Arreola, 2007; Smith, 2008). Strong defense of faculty values like freedom and autonomy, too, may be shielding faculty from scholarly-based methods of self-evaluation.

Constructive Suggestions for Administrators, Chairs, and Program Directors Based on our review of reactance theory, one suggestion we favor is encouraging administrators and chairpersons to involve faculty members directly in the development of any assessment plans. Specifically, we urge that administrators and chairs offer their faculties choices regarding how assessments will be conducted and used to inform teaching and learning at the departmental as well as institutional levels. By doing so, faculty members will have the opportunity to discuss the options and their respective merits, as well as to vote for their favored choice. This procedure has the advantage of maintaining faculty autonomy and also ensuring that faculty colleagues have direct input into any formalized assessment plans. Naturally, other new approaches to educating faculty about assessment matters should be considered, including borrowing ideas that work well with students. For example, there is much evidence to support the use of student learning community programs as a way to enhance student learning (e.g., Gabelnick, MacGregor, Matthews, & Smith, 1990; Hurd, 2004; Smith, 2008) and, arguably, to promote students’ autonomy (see Reeve, this volume). A student learning community is a group of first or second year undergraduates united by the exploration of a common academic topic. Our working assumption is that relevant findings and techniques from student learning communities can be generalized to the realm of educators themselves. Cox and Richlin (2004) reviewed the practice of what has come to be known as faculty learning communities (FLC) across institutions in the United States, which provide evidence for better retention, deeper learning, and greater civic participation for those involved. A FLC is a group of colleagues (usually numbering between 8 and 12) from different disciplines who take part in an active and collaborative program aimed at improving the teaching and learning in their courses. Beyond introducing and educating faculty about assessment practices and educational reform, FLCs are continuing education programs that can be adapted to investigate and discuss new methods of teaching in scholarly ways among faculty peers while reviewing and reflecting on student evaluations. Further, FLCs can help to strike a balance between what is desired by students, administrators, and faculty, all of whom may hold different views on what needs to change or be improved upon (Ballantyne, Borthwick, & Packer, 2000; Shao, Anderson, & Newsome, 2007).

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Faculty members who are provided with the opportunity to form learning communities can voice their opinions and work together toward common teaching and learning goals without administrative pressure. As a result, faculty reactance to assessment plans may be substantially reduced. By taking part in the outcome-oriented system of a learning community, faculty members will be more likely to respond favorably to arguments for assessment reform than if they remain geared toward the defense of their valued freedoms (Maio & Olson, 1995).

Future Directions for a Social Psychology of Assessment Readers will note that we have presented little, if any, data directly demonstrating reactance or attitude change in the context of educational assessment. Instead, we have relied on observation on our own campuses and those we have visited, as well as anecdotal evidence gleaned from other sources, including faculty and administrator colleagues from other schools. So far as we know, no empirical data on faculty reactance to assessment efforts exists. We do know that around the United States, faculty routinely collect assessment data, just as campuses launch initiatives aimed at improving teaching and learning in the classroom virtually every semester. Our hope is that motivated colleagues, especially department or program chairs or assessment officers, will consider using the framework laid out in this chapter to both plan strategies for introducing assessment activities and for collecting data that reveal what does (or does not) succeed on their campuses. Assessment is about productive change that benefits students, faculty members, and academic programs (Dunn et al., 2007). Opportunities for sharing the results of social psychologically-based research abound. Beyond traditional disciplinary conference venues (e.g., psychology, education), motivated colleagues can present their work in settings dedicated to interdisciplinary or other general education ventures. The budding arena of inquiry known as the scholarship of teaching and learning (SoTL), as well as the print and online journals dedicated to it, would also be fine outlets for such work. Perhaps the most important outcome here is to identify those practical practices that overcome faculty resistance while promoting collegial relations within an institution and also maintaining autonomy over what happens in the classroom. We look forward to learning about and sharing in our colleagues’ discoveries.

Acknowledgments Portions of this work were written during the first author’s spring 2009 sabbatical leave from Moravian College. This work was partially supported by a Moravian College SOAR grant to the first author in support of the last author.

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10 Applying Social Psychology in the College Classroom Teachers and Learners Need (Your) Scholarship Randolph A. Smith

I am pleased and honored to have the opportunity to have the “last words” in this book linking teaching with social psychology. Although they may be the last words in this book, I feel confident in predicting that they will not be the last words on this topic. In a sense, this book is an outgrowth or extension of Rick Snyder’s (2005) special issue of the Journal of Social & Clinical Psychology, much of which also dealt with the interface between teaching and social psychology. Snyder pointed out that he was hoping to begin a new interface of social and clinical psychology as applied to teaching. As Halpern and Desrochers (2005, p. 52) wrote in that special issue, applying social psychology principles is “a primary tool for crafting instructional designs that foster learning. It also gives professors a chance to apply social psychology to their teaching.” This volume provides ample illustrations of both of those principles, thus building on Snyder’s special issue. Although social psychology falls squarely within the discipline of psychology, the scholarship of teaching and learning (SoTL) fits within any and all disciplines. This volume represents an attempt to acquaint nonpsychologists with social psychology principles so that they can apply those principles in both their classrooms and their SoTL agendas. In this closing chapter, I revisit each chapter in this volume to both emphasize important points from the chapters and to go beyond the ideas presented there. My goal is not only to help readers think again about the issues involved, but also to challenge readers to think about applying principles from the chapter to their teaching and scholarly work. For readers who simply wish to glean classroomapplicable principles for their teaching, this volume is a gold mine. For readers who

Empirical Research in Teaching and Learning: Contributions from Social Psychology Edited by Debra Mashek & Elizabeth Yost Hammer © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33719-8

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wish to delve into SoTL themselves, I hope to help add ideas to those found in the chapters. Gurung and Burns (this volume) provide an excellent introduction to the volume with their definitions and histories of the scholarship of teaching and learning (SoTL) and social psychology. They issue a similar call to that of Snyder’s (2005)—to use social psychology in pedagogical research. Their call is an outgrowth of the conference that led to this book. Along with Gurung and Burns, I believe that many faculty do use social psychology concepts and principles in their classes, although they may not realize it. Thus, I believe the purpose behind these calls to action, as well as this volume and the special issue of Journal of Social & Clinical Psychology, is to move social psychology more directly into the consciousness of faculty—not just psychology faculty, but faculty in all disciplines. The proponents of linking social psychology with classroom behaviors would like faculty to be aware of how social psychology principles can enhance the enterprise of teaching and to use those principles intentionally.

Scholarly Teaching and the Scholarship of Teaching and Learning As faculty, we tend to “tinker” with our courses every time we teach them. That is, we may think back to the last time we taught the course and try to remember what worked well and what did not go so well. Almost without fail, something we did in the class did not work as well as we had hoped, so we tinker with that aspect of the course, changing it in some way that we hope will work better in the next version of the class. In a sense, we are conducting a research project with our class by introducing an independent variable, although we may not think of our actions in these terms. Faculty vary widely in how systematically and intentionally they implement such changes—in fact, each of us as faculty probably varies in our approach from course to course, from semester to semester. If we do not think about making these changes to a class with a research mindset, then we are probably less likely to be particularly intentional and systematic in making those changes. Thus, we may be ill-equipped to determine the effect or impact of the changes that we make. In this less intentional type of approach, although we are making well-intentioned changes to our classes, we are not engaging in what Richlin (2001) referred to as scholarly teaching. Richlin (2001) has drawn a distinction between scholarly teaching and the scholarship of teaching. In her conceptualization (see Figure 10.1), Richlin described scholarly teachers as teachers who systematically and intentionally make changes to their classes—they may scour the teaching literature to get ideas about how to teach a particularly thorny concept in a class. They may try a new technique or approach in one class, but not another, so that they have a way to compare and

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Figure 10.1 Richlin’s conceptualization of scholarly teaching and the scholarship of teaching Source: Richlin, 2001, p. 59.

assess the effect of those changes. They may compare student performance from a class that experiences an innovation to a previous class that did not experience the innovation, again so that they can actually assess whether the change made a difference. Thus, when we make haphazard changes to our classes without being intentional and systematic, even with good intentions, we are not engaging in scholarly teaching. The desire of Snyder (2005) and Gurung and Burns (this volume) to have faculty use social psychology intentionally in the classroom is, to me, reminiscent of Richlin’s idea of scholarly teaching. Rather than accidentally using social psychology concepts and principles in classes, these authors have asked faculty to be intentional and systematic in applying the ideas of social psychology to their classes. This volume provides us as faculty with some good examples and role models for making such applications. This volume also goes beyond simply encouraging faculty to be scholarly teachers in their applications of social psychology in the classroom. Richlin’s (2001) ideas have provided me with one of the major influences on my thinking about SoTL. Beyond simply discussing scholarly teaching, Richlin contrasted scholarly teaching with the scholarship of teaching. By examining Figure 10.1, you can see the difference in the two concepts. The scholarly teacher decides to implement a course change, consults relevant literature, gathers baseline information, selects and uses a modification, observes and documents the students’ behavior, analyzes the results, and checks those results against the baseline. However, scholarly teachers keep this information to themselves; in this manner, the information that they glean from their class innovations will benefit only their students in their classes. Teachers who engage in SoTL, on the other

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hand, also engage in the process depicted in the bottom portion of Figure 10.1— they submit their scholarship and scholarly products for peer review and scrutiny. When they publish their scholarship, then it becomes available for other faculty to use, thereby increasing the likelihood that more faculty will engage in scholarly teaching. This volume will contribute to this process by making more faculty aware of ways that they can apply social psychological findings to their classrooms in scholarly ways. Ideally, some of those faculty will turn their scholarly teaching into SoTL, thus extending the life and reach of these chapters. As I noted earlier, SoTL is not merely a psychology-based enterprise—it is interand multidisciplinary. Richlin’s academic background is in higher education—her model fits within any academic discipline. As you read this volume, if you have doubts about the applicability of SoTL to your discipline, I urge you to access a list of scholarly journals that deal with the teaching/learning enterprise from virtually any discipline in higher education (see list at http://www.kennesaw.edu/cetl/ resources/journals.html). This listing contains journals in 38 academic fields that publish SoTL-type articles. Surely your discipline is on the list—if not, at least a closely allied discipline or subdiscipline probably is.

Reflections on the Chapters Gurung and Burns (this volume) provide interested readers with a wealth of ideas and topics for possible applications of social psychology to teaching and learning with their list of topics in Table 1.1. If you are not already engaged in the process of scholarly teaching or SOTL, I encourage you to peruse this table for teaching ideas that you can link to social psychology principles and that you can begin to study in a systematic fashion. As I scanned Table 1.1 and the authors’ suggestions of teaching and learning applications for each concept, I got the sense of a great many applications and subsequent research projects dealing with social psychology principles in the classroom. In the remaining chapters of this book, we see some of those principles actually applied to the teaching/learning environment. I hope that my summary and exposition on each chapter may provide readers with ideas that did not occur to them as they originally read the chapters—for teaching their classes, for conducting SoTL research, or both. Wilson, Naufel, and Hackney (this volume) examine some important aspects of the teacher–student relationship to determine their effects on that relationship. They present social psychology research findings concerning social connectivity, first impressions, and expectations and argue that all of these concepts can help to inform teaching and learning. Wilson et al. point out that forming positive social connections with other people is linked to a variety of positive outcomes. One does not have to teach too long to realize that at least some students are interested in forming connective relationships with some of their faculty. In Buskist and his

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colleagues’ research on master teaching (e.g., Buskist, Sikorski, Buckley, & Saville, 2002; Schaeffer, Epting, Zinn, & Buskist, 2003), students consistently rate teachers being personable and approachable as an important quality/behavior that good teachers demonstrate. Benson, Cohen, and Buskist (2005) found that students counted rapport with faculty as important to them and reported many positive academic behaviors based on experiencing rapport with a faculty member (e.g., attending class, enjoying the subject, studying for class). Appleby (e.g., 2002) has written extensively about advising students, which is one way that we as faculty can connect with our students. Appleby maintains that advising is simply an extension of our role as teachers. Although we know a great deal about the importance of social connectivity between faculty and students, there is ample room for more work in this area. Most people have heard about the importance of first impressions since they were children, and social psychology research has confirmed that importance. In fact, it is almost scary how quickly people form first impressions and how lasting those impressions are. It requires little difficulty generalizing from this social psychology research on impressions to decide that the first day of class is probably highly important in terms of students forming first impressions of their faculty. A search for “first day of class” in PsycINFO (the primary database of psychological research) yielded 40 hits, which indicates a fair amount of research on the topic. Legg and Wilson (2009) have even stretched the definition of “first day” by showing positive effects of an e-mail from the instructor that students received a week before classes began. Some effects persisted throughout the entire semester, at least for female students. This type of evidence clearly indicates that the classroom is an arena in which first impressions can be just as critical as in other aspects of life. SoTL projects could help to better understand the parameters of the first impressions variable. Research has clearly shown that expectations color people’s impressions of people and things in the environment. Much like first impressions, expectations are difficult to reverse or undo. The examples that Wilson et al. (this volume) provide focus on the effect of teachers’ expectations for students. The effects of students’ expectations for teachers are likely to be just as important. For example, Bock (1979) showed that students whose expectations for a course were met were more likely to rate the instructor of that course as effective when compared to students whose expectations were not met. As faculty, do we have any idea of the preconceived expectations students have of us when we walk into the classroom on the first day of class? It is likely that students talk among themselves about faculty, thus setting up expectations before classes even begin. In a similar vein, do faculty who teach courses that tend to be student favorites (e.g., applied courses, interesting topics, controversial issues) receive the benefit of favorable student expectations before they ever set foot in class when compared to faculty who teach courses that are not student favorites (e.g., difficult classes, courses that involve

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math, required/general education courses)? There are many interesting questions to ask regarding expectations—both from student and faculty points of view. Continuing with the importance of expectations, Hurley and Hurley (this volume) address the issue of culture in higher education, focusing on the increasing diversity in the student population. They contend that differing views of self-construal among some cultures imply that some diverse students will not share the view of the world that seems to predominate in academia. If we in higher education are truly going to welcome diversity with open arms, it is our responsibility to bring about institutional change to welcome students with worldviews that are different from ours. In terms of success, Hurley and Hurley contrast independent and interdependent views of self-construal. People who are independent tend to focus on their own success whereas people who are more interdependent tend to be more concerned with the success of the group. This classification seems quite similar to the distinction between individualism and collectivism (Adamopoulos, 1999). Hurley and Hurley maintain that higher education tends to be oriented toward an independent self-construal in that colleges and universities tend to emphasize independent learning and achievement. This emphasis on independence is even reflected in the reward structure of higher education through individual recognitions such as making the Dean’s or President’s List and graduating with honors. Hurley and Hurley point out that the fastest growing ethnic groups in the college population are more likely to have interdependent self-construal than White students. Thus, we as faculty may encounter students who do not share our values about optimum academic behaviors. This mismatch might result in lower academic performance or a preference for group activities on the part of students who have an interdependent self-construal. In such cases, faculty must guard against the peril of making internal attributions about students—rather than a problem existing within the students, the problem would be a situational one in that they do not share the values that the faculty and institution do. Another interesting dilemma for faculty is the apparent contradiction between Hurley and Hurley’s recommendation and that of Reeve (this volume). If we strive to make the institution accommodate to students with interdependent self-construal, this action seems at odds with the idea of helping students to become more autonomous in their learning. On the other hand, of course, the two pieces of advice might not be contradictory, but simply represent an interaction of sorts. It may be that we as faculty need to work with interdependent students in different ways than we work with more independent students. If this conclusion is correct, the job of becoming a faculty member has just become more complex. However, another possible interpretation of these two recommendations is that instead of being contradictory, they are actually complementary. For example, if given the option, it may be that students with high levels of interdependence might use a sense of autonomy that they have acquired to choose to participate in group projects, assessments, collaborations, and the like. This

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possibility sends a message to us as faculty that we might need to develop new types of classroom activities or assessment approaches. Also linked to the importance of expectations, Hammer (this volume) points out that cross-racial teaching creates a situation in which it is easy for either teachers or learners (or both) to fall back on stereotypes that they hold of other races and react based on those stereotypes rather than reacting to people as individuals. As higher education steadily becomes a more diverse arena, it is important to take Hammer’s message to heart—and particularly important to remember that stereotypes do work in both directions. Although majority group stereotypes of minority groups tend to receive the most attention, minority groups do tend to stereotype the majority also. Hammer’s data show that although Black students tend to endorse having White professors teach them at HBCUs, they are not certain that White faculty are as concerned about issues that affect Black students or that White faculty can relate to Black students as well. Findings such as these are ripe for further investigation by academics, both faculty and students. Although stereotypes are a psychological concept, they have influence across the campus in all disciplines—perhaps, even, in some disciplines more than others. Hammer also covers the social psychology concept of stereotype threat in which members of a negatively stereotyped group may be adversely affected by existing stereotypes and fail to perform as well as they might have. Although the majority of stereotype threat research has focused on minority groups, the definition of the term allows for a focus on any negatively stereotyped group. Thus, it would be interesting to conduct research examining White faculty who teach at HBCUs— might they also fall victim to stereotype threat? For example, given Hammer’s data mentioned previously about stereotypes that Black students hold of White faculty, might White faculty at HBCUs who are interested in issues that affect Black students begin to show lessened interest due to stereotype threat? Stahl (2005) also shared her experiences of teaching at an HBCU. She wrote eloquently about the challenge of involving cross-racial students in research, particularly given her training as an animal researcher. Referring to the work of Anderson and Lancaster (1999), Stahl (p. 92) stressed the importance of allowing students to have some say about the area of research in which they will become involved: “it is up to me to help the students to quickly transverse from what these authors call the `silenced voice´ to the `transformed voice´.” In other words, Stahl endorsed the self-determination theory (SDT; see also Reeve, this volume) concept of student autonomy in choosing a research topic or area. This application of student autonomy is one that I strongly endorse. As I taught the research sequence courses at a small liberal arts college in Arkansas for 26 years, the requirement in place was for all majors to develop and conduct a research study. Although the process was sometimes painful for students who were not motivated or who had little direction, I required them to develop an original piece of research based on their interests. For a project that was going to last for two semesters, student

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autonomy was, I believe, crucial to keep the students interested and on track. Working to increase student autonomy is probably important for any class project assigned to students, regardless of the discipline involved. Given the potential for stereotypes to “invade” cross-racial teaching, I suspect that providing students with great autonomy is all the more important. Building on this theme of autonomy, Reeve (this volume) discusses selfdetermination theory (SDT) and its applications to teaching and learning. SDT is a theory of motivation that focuses on the individual’s striving for psychological growth and development and assumes that people attempt to master life challenges and incorporate their experiences into their sense of self. SDT (http://www.psych. rochester.edu/SDT/index.php) assumes that one’s social environment can either support or impede this striving process. Reeve notes that SDT can apply to the teaching/learning process in the area of student autonomy—he argues that student learning will be most effective when faculty support students’ autonomy during the learning process. In this context, it becomes important for faculty to help students rely on their inner motivational resources. This approach reminds me of the quotes (unknown source) labeling opposing viewpoints of teaching as being either the “sage on the stage” (professors who consider it their job to provide all the information for the students) or the “guide on the side” (professors who give students the freedom or opportunity to discover and learn on their own). It seems clear that a sage on the stage is much less likely to help foster student autonomy than a guide on the side. Reeve’s contrast of a controlling motivational style with autonomy support reminds me of the social psychology research on obedience. Obedience research (e.g., Milgram, 1963) has shown that people are amazingly obedient to an authority figure. If the generalization of obedience findings to Reeve’s application of SDT to the teaching/learning enterprise is a valid one, then it would seem logical to predict lower levels of student autonomy being associated with a teaching style that is more geared toward doling out information rather than allowing students to engage in more active learning, hands-on, or discovery approaches. This notion is consistent with Carl Rogers’s lament (cited by Reeve) that, in teaching, he believed that he robbed learners of their internality—in essence, their autonomy. The SDT Web site lists “education” as a potential application area for SDT, and Reeve cites relevant information for the SDT-teaching/learning link, so this area is one that still has great potential for more teaching/learning research. Given the variability in human behavior and motivation, is it the case that all students will thrive in an autonomyfriendly classroom—or are some students more prepared to deal with autonomy than others? If the latter is true, what can faculty do to encourage or nurture autonomy in the students who are not equipped to handle it? These are just a few examples of autonomy-related teaching/learning questions that come to mind. Student autonomy is also relevant to the chapter by Clevering, DeFord, Yamamura, and Mashek (this volume)—they recount a study in which they

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examined the relations among attitude toward seeking help, actually seeking help, and academic achievement. This research is reminiscent of social psychology research linking attitudes and behavior. Although people typically expect that attitudes lead to and can predict behavior, social psychological research shows that this assumption is often incorrect—in fact, there is ample research evidence that behaviors may actually lead to attitudes. Clevering et al.’s study is intriguing because it linked an attitude (about seeking help) to a behavior (actually seeking help) and then to a subsequent outcome that is at least to some extent rooted in behavior (academic performance). Interestingly, Clevering et al. found a stronger relation between attitude toward seeking help and achievement than between seeking help and achievement. Thus, the attitude was more predictive of a behavior one step removed than the behavior that logically seems to follow directly from the attitude. This relation seems to contradict the typical finding regarding specificity of attitude and behavior prediction. Research has shown that more specific attitudes are better predictors of behavior than general attitudes (e.g., Newcomb, Rabow, & Hernandez, 1992). Newcomb et al. found that antinuclear (specific) attitudes were better predictors of antinuclear political activism than pacifist (general) attitudes. The range of potential applications of the question of attitudes-behavior link to the teaching-learning process is vast. As an example, Taraban, Box, Myers, Pollard, and Bowen (2007) had high school and college faculty collaborate to develop two active-learning exercises for high school biology labs and contrasted the use of these exercises with more traditional lab exercises. Teachers’ records indicated that they used fewer independent activities and worksheets and more active and labbased activities with the new labs. Students perceived greater learning gains in the new labs compared to more traditional labs. Faculty perceived that they changed their behaviors in response to the student-centered principles. Based on the notion that behavior change often leads to attitude change (e.g., Bem’s self-perception theory, 1972), it is predictable that the teachers would develop more favorable attitudes toward student-centered learning. Halpern and Stephenson (this volume) examine the application of the science of teaching to the art of learning. Focusing on laboratory and classroom research, Halpern and Stephenson emphasize the importance of teaching critical thinking (and the transfer of those critical thinking skills) and employing learning principles that promote transfer. Although these two concepts are certainly not exclusively the “property” of social psychology (or even psychology in general), we can use social psychology to examine them in the teaching/learning context. Having taught a course in social psychology for many years, that course seems particularly prone to post hoc “common-sense” explanations by students. After reading or hearing about a particular study, students are often quick to make comments such as “Oh sure, that makes sense” or “That’s what I thought would happen.” For example, Festinger and Carlsmith (1959) conducted a famous study

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dealing with cognitive dissonance—an uncomfortable motivational state people experience when they display inconsistency among their attitudes, beliefs, and behaviors. Festinger and Carlsmith had participants engage in an extremely dull and boring task. Afterward, the researchers asked the participants to lie to a prospective participant by telling this person that the experiment was fun and interesting—the participant received either $1 or $20 for telling the lie. Later, in a different context, the original participants completed a questionnaire about their research participation and indicated how enjoyable their experience was. Results showed that participants who received $1 for lying enjoyed the experiment more than participants who received $20, which is a counter-intuitive finding. Festinger and Carlsmith explained that participants who received only $1 for lying had to find some justification for their behavior other than the money, given that it was such a small sum. Thus, they apparently convinced themselves that they really did enjoy the experiment, which minimized the impact of them having lied. The important point regarding teaching is that students typically nod knowingly after hearing the results, apparently believing that they “knew” that would be the outcome. The teaching approach of lecturing and imparting the information (“sage on the stage”) does not engage students’ critical thinking skills, so I have changed my approach. Now when I talk about Festinger and Carlsmith’s research, I explain the study but, rather than simply telling students the results, I have students predict the results beforehand. Unless students have read ahead in their text, virtually all of them predict that the participants paid $20 will indicate stronger liking for the experiment. Now I have “hooked” them, so I can “reel” them in by showing the actual results and having them engage in critical thinking about the outcome. The goal of this approach is to increase the likelihood of Halpern and Stephenson’s concept of far transfer through the strategy of having students generate responses. Ideally, students learn not to assume or believe that they “knew it all along” (Myers, 2010, p. 13). I know that social psychology and psychology are not the only academic areas that suffer from this problem. It is up to us as faculty to develop strategies for our classes to help students get past their common-sense notions of our disciplines or their “I knew that all along” impressions. Testing the strategies that you develop for your classes helps you to engage in scholarly teaching; sharing tested strategies with others through presentation and publication becomes SoTL. Halpern and Stephenson cover seven evidence-based principles of learning that are known to promote transfer. Some of these principles align more closely with social psychological principles than others. For example, the first of the principles they mention, clarify objectives, seems intricately linked to a concept I have highlighted several times in this chapter—autonomy. One of the major rationales for providing students with learning objectives is so that they will know what they are responsible for learning. Providing students with a “road map” to their learning enables them to become more autonomous—rather than having to guess

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whether they know the material that will be on an exam, they can instead measure themselves against the learning objectives for that exam. I strongly suspect that students vary widely in how much they use (and perhaps how efficiently they use) learning objectives that faculty provide for them. Based on the research that Halpern and Stephenson cite about learning objectives, it seems clear that more students could benefit from using learning objectives—the key question is what we, as faculty, need to do in order to get students to use these objectives and to use them effectively. This question is something that you can attempt to answer for yourself. Halpern and Stephenson also mention the importance of challenging learners’ epistemology and specifically discuss expectancy theory. There is a good deal of research about expectations in the teaching/learning arena, with some of the earliest research dealing with teachers’ expectations of students. Rosenthal and Jacobson (1968) reported a study in which they randomly selected some students and informed their teachers that these students showed promise for intellectual blossoming in the coming year on the basis of (fictitious) test scores. Although the children were randomly selected, they did indeed show marked improvement on their next years’ IQ testing. Subsequent research has shown that teacher expectations are not destiny, but high teacher expectations do seem to benefit weaker students (Myers, 2010). Interestingly, expectations can work in the opposite direction also—student expectations of faculty also play an important role in the teaching/learning process. Feldman and Prohaska (1979) simulated a classroom situation in which an “instructor” taught a lesson to a group of students (participants in the experiment). The students expected the instructor to be either effective or ineffective based on information given to them beforehand. The students taught by the “effective” instructor showed more positive attitudes and nonverbal behaviors, and they actually learned more relative to the students taught by the expected ineffective teacher. This research, of course, is relevant to faculty in all disciplines and certainly demands our attention. Dealing with our students fairly and equitably demands that we take as many precautions as we can to avoid developing (or acting on) expectations about students. The more difficult task, of course, is managing students’ expectations of us or our courses (as I mentioned earlier in discussing Wilson et al.’s chapter). Following on Halpern and Stephenson’s look at the science of teaching, Hattie (this volume) refers to his work in synthesizing over 800 meta-analyses (over 52,000 studies) related to teaching and student achievement (Hattie, 2009). The results showed three strategies clearly linked to maximizing achievement: (a) communicate clear learning intentions and criteria for success; (b) use multiple teaching strategies that emphasize student perspectives in learning; and (c) seek feedback regarding teaching effectiveness and give students feedback regarding their learning effectiveness. Although none of these three strategies are specifically social psychology concepts, they do relate to other concepts discussed by authors in

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this volume. All three strategies tie in to the notion of social connectivity that Wilson et al. discussed, as they would all help to establish a relationship between faculty and students. Buskist et al. (2002), for example, listed “realistic expectations” as the highest rated behavior for good teachers. “Encourages/cares for students” also made the top 10 at number 8—it certainly seems reasonable that students would interpret the strategies listed by Hattie as fitting within encouraging and caring for students. Students rated “provides constructive feedback” as the 13th highest behavior. Hattie’s “big three” also relates to Reeve’s discussion of the SDT concept of autonomy. By engaging in the strategies that Hattie listed, faculty would be giving students the opportunity to develop more autonomy by being more responsible for their learning. It is impossible to chart a path toward autonomy if you have no idea where you are going; Hattie’s strategies would clearly allow students the chance to develop a “road map” for where they should be heading, which dovetails nicely with Halpern and Stephenson’s point about clarifying objectives. Focusing on faculty rather than on students, Dunn, McCarthy, Baker, Halonen, and Boyer (this volume) have compiled an excellent application of social psychology to teaching. They interpret faculty’s reluctance (resistance?) to engage in assessment activities in light of reactance theory. Although this reluctance may seem ironic or counterintuitive at first, thinking of it as reactance makes a great deal of sense. Having served as a department chair at three different institutions for over 25 years, I can attest (as can any academic administrator) that faculty, as a group, have an extremely strong sense of individual control—faculty probably do not need any help learning to be autonomous! When that sense of control is threatened, faculty often respond fairly strongly (the textbook definition of reactance). Interestingly enough, I feel certain that if faculty had developed the idea of outcomes assessment, rather than having it imposed by accrediting agencies, that they would think it was a great idea. Not only did Dunn et al. use a social psychology principle to explain faculty reluctance to assess outcomes, they also used social psychology to give suggestions for how to overcome this reluctance. Citing just one of their proposed solutions, using the elaboration likelihood model and heuristic-systematic model of attitude change would imply that anyone trying to convince faculty of the value of some change should endeavor to have the faculty engage in effortful processing. This explanation probably helps to explain why resorting to an appeal to authority (e.g., “The accrediting body mandates that we assess outcomes, so we need you to cooperate.”) is usually less-than-convincing to faculty—listening to an authority figure tell you what to do is not a particularly engaging activity. Higher education, in my opinion, has more than its share of strong-willed and strong-minded people who prize their individual freedom, from upper level administrators to students. Thus, academe is an environment that is probably highly susceptible to the phenomenon of reactance. Isn’t it likely that administrators

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react when faculty “corner” them and take away some of their options for action? When faculty make assignments with rigid guidelines and submission dates, might students experience reactance? It seems to me that higher education is rife with opportunities to apply reactance theory to research questions.

Conclusion In closing, let me reiterate that I hope you take advantage of the ideas put forth by the authors in their chapters. They have made strong and persuasive cases for the value of applying social psychology principles to the teaching/learning enterprise. Most importantly to me, the suggestions that they have put forth are not simply ideas that they have about approaches that might be useful in the classroom. Rather, they made these suggestions based on empirical research—so these suggestions have the power of evidence backing them. Because of the empirical nature of these suggestions, by reading about them, you have taken the first step toward scholarly teaching—consulting the research literature (see Figure 10.1). As soon as you choose an intervention to implement, you have taken the second step. If you continue on in an intentional and systematic fashion and use your findings to inform your teaching practices in the future, you will be engaging in scholarly teaching, a commendable step indeed. As I have tried to make clear throughout this concluding chapter, changing your scholarly teaching to SoTL simply requires you to hold your scholarly teaching up to the light of peer review. I encourage you to take this final step and to help contribute to the growing body of knowledge of empirical research on teaching and learning. Happy teaching! I encourage you to make your teaching scholarly and turn your scholarly teaching into SoTL products that other teachers can use for their benefit and for the benefit of their students.

References Adamopoulos, J. (1999). The emergence of cultural patterns of interpersonal behavior. In J. Adamopoulos & Y. Kashima (Eds.), Social psychology and cultural context (pp. 63–76). Thousand Oaks, CA: Sage. Anderson, T. P., & Lancaster, J. S. (1999). Building conversations of respect: The voice of White faculty at Black colleges. In L. Foster, J. A. Guyden, & A. L. Miller (Eds.), Affirmed action: Essays on the academic and social lives of White faculty at historically Black colleges and universities (pp. 165–174). Lanham, MD: Rowman & Littlefield. Appleby, D. C. (2002). The teaching–advising connection. In S. F. Davis & W. Buskist (Eds.), The teaching of psychology: Essays in honor of Wilbert J. McKeachie and Charles L. Brewer (pp. 121–139). Mahwah, NJ: Lawrence Erlbaum.

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Bem, D. J. (1972). Self-perception theory. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 6, pp. 1–62). New York: Academic Press. Benson, T. A., Cohen, A. L., & Buskist, W. (2005). Rapport: Its relation to student attitudes and behaviors toward teachers and classes. Teaching of Psychology, 32, 237–239. Bock, F. A. (1979). Student expectations of course content affect faculty evaluations in an abnormal psychology course. Teaching of Psychology, 6, 167–169. Buskist, W., Sikorski, J., Buckley, T., & Saville, B. K. (2002). Elements of master teaching. In S. F. Davis & W. Buskist (Eds.), The teaching of psychology: Essays in honor of Wilbert J. McKeachie and Charles L. Brewer (pp. 27–39). Mahwah, NJ: Lawrence Erlbaum. Feldman, R. S., & Prohaska, T. (1979). The student as Pygmalion: Effect of student expectation on the teacher. Journal of Educational Psychology, 71, 485–493. Festinger, L., & Carlsmith, J. M. (1959). Cognitive consequences of forced compliance. Journal of Abnormal and Social Psychology, 58, 203–210. Halpern, D. F., & Desrochers, S. (2005). Social psychology in the classroom: Applying what we teach as we teach it. Journal of Social and Clinical Psychology, 24, 51–61. Hattie, J. A. C. (2009). Visible learning: A synthesis of 800 meta-analyses relating to achievement. Abingdon, UK: Routledge. Legg, A. M., & Wilson, J. H. (2009). E-mail from professor enhances student motivation and attitudes. Teaching of Psychology, 36, 205–211. Milgram, S. (1963). Behavioral study of obedience. Journal of Abnormal and Social Psychology, 67, 371–378. Myers, D. G. (2010). Social psychology (10th ed.). New York: McGraw-Hill. Newcomb, M. D., Rabow, J., & Hernandez, A. C. R. (1992). A cross-national study of nuclear attitudes, normative support, and activist behavior: Additive and interactive effects. Journal of Applied Social Psychology, 22, 780–800. Richlin, L. (2001). Scholarly teaching and the scholarship of teaching. In C. Kremer (Ed.), Scholarship revisited: Perspectives on the scholarship of teaching and learning (pp. 57–68). San Francisco: Jossey-Bass. Rosenthal, R., & Jacobson, L. (1968). Pygmalion in the classroom: Teacher expectation and pupils’ intellectual development. New York: Holt, Rinehart & Winston. Schaeffer, G., Epting, K., Zinn, T., & Buskist, W. (2003). Student and faculty perceptions of effective teaching: A successful replication. Teaching of Psychology, 30, 133–136. Snyder, C. R. (2005). Dispelling the fable of “Those who can, do, and those who can’t teach”: The (social and clinical) psychology of instruction [Special issue]. Journal of Social and Clinical Psychology, 24(1). Stahl, J. M. (2005). Research is for everyone: Perspectives from teaching at historically Black colleges and universities. Journal of Social and Clinical Psychology, 24, 85–96. Taraban, R., Box, C., Myers, R., Pollard, R., & Bowen, C. W. (2007). Effects of activelearning experiences on achievement, attitudes, and behaviors in high school biology. Journal of Research in Science Teaching, 44, 960–979.

Index

Abrami, P. C. 131 accountability 16, 24, 66 Active Learning in Higher Education 5 Adamopoulos, J. 165 Adams, G. 56 Adonu, J. 56 affirmative action 73 African Americans see cross-racial teaching; self-construal aggression 12, 14, 15, 20, 38 Ahern, J. 23 Albarracin, D. 150 Albury, A. 60 Aleamoni, L. M. 144 Alexander, P. A. 6, 140 alienation, feelings of 54 Allen, B. A. 57, 62 Allen, M. 38 Allport, Gordon W. 10, 145 Alper, C. M. 34 Altbach, P. G. 51, 53, 54 Altman, I. 37 Ambady, N. 21, 39, 81 American Psychological Association 3, 7; National Conference on Undergraduate Education in Psychology 10 Andersen, J. F. 37, 38 Anderson, C. A. 42 Anderson, J. L. 9 Anderson, L. P. 155 Anderson, S. 56

Anderson, T. P. 166 Angelo, T. A. 145 anonymity 16 Ansburg, P. I. 6, 140 Apfel, N. 84 Apple, K. J. 146 Appleby, D. C. 164 Argyle, M. 55 Aristotle 3 Armour-Thomas, E. 55 Aron, A. 58, 61 Aron, E. N. 58, 61 Aronson, J. 81, 82 Arreola, A. R. 155 Arum, R. 52 assessment: applying cognitive psychology to 9, 119–20; cross-racial teaching 81–2, 86; overcoming faculty reluctance 143–59, 171–2; teaching and learning strategies 138–9, 140 Assor, A. 92, 93, 94 attitude change 14; elaboration likelihood model 151, 171; heuristic-systematic model 151, 171; persuasion and assessment 150–1, 154, 171 attitudes 12, 19; culture and race 14–15, 51–72, 165; help-seeking 104–13, 167–8; teacher-student relationship 37, 38, 40–1, 44 attraction 12, 19, 22, 39, 43; “What is beautiful is good” effect 39, 41, 44 attributes 44

Empirical Research in Teaching and Learning: Contributions from Social Psychology Edited by Debra Mashek & Elizabeth Yost Hammer © 2011 Blackwell Publishing Ltd. ISBN: 978-1-444-33719-8

Index attribution theory 16, 18, 84 attributional biases 16 attributional styles 16 autonomy: faculty 143, 144, 149, 150–4, 155, 156, 171; students see student autonomy Avni-Babad, D. 21 Babad, E. 21, 43 Bahrick, H. P. 9 Bain, K. 21, 23 Baker, S. C. 144, 146, 171 Ballantyne, R. 155 Banaji, M. R. 39 Barch, J. 93 Barron, K. E. 146 Bartlett, Sir Frederick 122 Baruch-Feldman, C. 35 Bass, R. 2 Bateman, M. 145 Batey, M. 110 Baumeister, R. F. 32, 33, 34, 35, 36, 39, 79 behavior: and attitudes 168; and social connectivity 36; see also help-seeking attitudes and behavior belief perseverance 16, 42 Belmont, M. J. 94 Bem, D. J. 168 Ben-Dayan, D. 35 Benson, T. A. 37, 164 Berdahl, R. O. 51 Berliner, D. C. 3 Bernieri, F. 43 Bernstein, D. 2, 8, 10, 127 Berscheid, E. 39, 42 bias see prejudice Biggs, J. 136 Billig, M. 32 biological markers 15 Bjork, R. A. 8, 9, 119, 122 Black, A. E. 94 Bloom’s taxonomy of educational objectives 118, 119 Bock, F. A. 164 body language see nonverbal cues

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Boice, R. 22 Bond, M. 55 Borkowski, J. G. 8 Borthwick, J. 155 Bowdle, B. 14 Bowen, C. W. 168 Bowers, J. W. 38 Bowman, P. J. 53, 54 Box, C. 168 Boyer, E. L. 3–4, 130 Boyer, S. 171 Boykin, A. W. 57, 60, 62 Bratslavsky, E. 35, 39 Braxton, J. M. 54, 65 Brehm, J. W. 143, 144, 146, 147, 148, 149, 150 Brehm, S. S. 143, 144, 147, 148, 149, 150 British Enhancing Teaching and Learning (ETL) project 5 Brondolo, E. 35 Brookfield, S. D. 23 Bruff, D. 138 Brzustoski, P. 84 Buckley, T. 164 Burgess, D. R. 40 Burgoon, J. K. 34 Burley, H. E. 136 Burns, K. C. 161, 162, 163 Buskist, C. 21 Buskist, W. 2, 7, 17, 21, 37, 40, 163–4, 171 Buss, D. M. 32–3 Butler, R. 105, 111 Butterfield, B. 9 Cacioppo, J. T. 151, 154 Cannaday, L. W. 61 Carless, D. 137 Carlsmith, J. M. 13–14, 168–9 Carlson, J. F. 17 Carnegie Academy for the Scholarship of Teaching and Learning (CASTL) 4 Carnegie Foundation for the Advancement of Teaching 4 Carrell, D. 93

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Carrell, L. J. 38 Catanese, K. R. 36 Cepeda, N. J. 8, 121 Cesar, O. P. 33 Chaiken, A. L. 43 Chaiken, S. 150, 151 Cham, J. 144 Chavous, T. M. 85 Cheong, Y. F. 106 Chew, S. L. 8, 17 Chick, N. 5–6 Chirkov, V. 94 Christensen, L. J. 38 Christophel, D. M. 38 Christopher, A. N. 17 Claremont Symposium on Applied Social Psychology 15 Clarke, J. S. 145 Claypool, H. M. 33 Clevering, J. 167–8 clinical psychology 10, 11, 16 Cloutier, J. 74 Coffield, F. 136 cognition 35, 36, 154; promoting transfer of critical thinking skills 116–27; see also social cognition cognitive dissonance 13–14, 19, 169 cognitive flexibility 122 cognitive psychology 11, 16, 18, 119–22; contribution to pedagogical research and scholarship of teaching and learning 8–10 Cohen, A. L. 164 Cohen, D. 14 Cohen, G. L. 83–4 Cohen, L. A. 37 Cohen, P. A. 131, 132 Cohen, S. 34 collaborative learning and teaching 58, 65–6, 112 collectivism 165; see also communalism color: attitudes to 14–15; expectations 42; institutional challenges 53–4; see also race Comenius, John Amos 3

communalism 57, 59, 60, 61–4, 65, 66 communalism scale 60, 61–2, 64 competition 56, 67 Comstock, J. 38 confirmation bias 18, 23, 41–2, 44 conformity 12, 20 Contarello, A. 55 contracts 16 Cooper, H. M. 131 Cooper, J. 42, 75 cooperative learning and teaching 58, 65–6, 112 Coulson , R. C. 122 counterfactual thinking 18 Coupland J. 33 Cox, M. D. 155 Crano, W. D. 150 Credle, J. O. 66 critical thinking 18–20, 116–27, 168–70; cross-racial teaching 87; principles of learning that promote 118–26, 127, 169–70; transfer of skills 116–17, 127 cross-racial teaching, unintentional prejudice in 73–89, 166, 167 Crusco, A. H. 34 Cull, W. L. 119 cultural mistrust 59, 60, 61, 62, 63, 64, 65 cultural psychology 54 culture 51–72, 165; attitudes to difference 14–15; institutional challenge of 53–4; self-construal and 54–67, 165; stereotypes 41; student diversity 51–3, 165 culture of honor 14 Daniel, D. 9 Das Carlo, M. D. 35 Davis, B. G. 1 Davis, L. H. 62 Dean, G. J. 66 Dean, K. 35 Deatrick, L. M. 149 DeBerard, M. S. 110

Index deCharms, R. 94, 101, 144 Deci, E. L. 90, 91, 92, 93, 94, 95, 101 decision-making 16 De Corte, E. 117 DeFord, S. 167–8 deindividuation 20 Delevati, N. M. 33 De Luca, B. M. 149 DeNisi, A. 124, 137 dePaulo, B. M. 37 Derbish, M. H. 9 Derlega, V. J. 43 Desrochers, S. 16, 160 Deutsch, M. 147 Devine, P. G. 39, 74, 82 DeVito, J. A. 36 Dewey, John 3 deWinstanley, P. A. 119 Dion, K. 39 distributed learning 121–2 Dochy, F. 101 Donovan, J. J. 121 Downs, A. C. 39 Doyle, W. J. 34 Drexel, St. Katharine 76 dual-coding 123–4 Dufresne, R. J. 116 Dunbar, R. I. M. 33 Dunlosky, J. 8 Dunn, D. S. 144, 145, 146, 156, 171 Duntley, J. D. 32–3 Dweck, C. S. 126 Eagly, A. H. 150, 151 Earley, P. C. 55 Eaton, L. 57 Ecclestone, K. 136 Edelmann, R. J. 55 education: application of social psychology 12; and PR 5; see also educational institutions; faculty; learning; teacher-student relationship; teaching educational institutions: and assessment 144, 147, 148, 149, 150–6,

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171–2; challenge of culture and diversity of students 52–4; self-construal in 56–7, 58, 65, 66–7, 165; student perspectives 76–87 educational psychology 3, 5, 8, 9, 104–5 ego depletion 16, 19, 79–80, 82 Einstein, G. O. 9 electronic communication: help seeking 108; teacher-student relationship 21, 36, 37, 41, 164 Ellett, C. D. 145 Ellison, C. M. 60 Ellsworth, P. 34 emotions 34–5, 36; negative affect 99–100, 101, 111 Entwistle, N. 5, 17 Epting, L. K. 21, 164 equity theory 16 ethnicity see race evaluation apprehension 145–6 exclusion, social connectivity 34–5, 36 expectancy bias 86 expectancy theory 126, 170 expectations 19, 165, 166, 170; science of teaching 170; teacher-student relationship 41–4, 163, 164–5, 166, 170 extrinsic motivation 91–2, 97 eye contact 34, 36, 42, 43 Facebook, and information exchange 114–15, 126–7 faculty: application of principles of learning 127; assessment 143–59, 171–2; cross-racial teaching 73, 76–87; expectations of 164–5, 170; recommendations for cooperative and collaborative teaching 65–6; scholarly teaching 161–3, 172; see also teacher-student relationship; teaching faculty learning communities (FLC) 155–6 false consensus effect 18, 23 familiarity 33, 36–7 Feagin, J. 54

178

Index

feedback: cross-racial teaching 82, 83–4, 87; learning to promote transfer of critical thinking 124–5; student autonomy 98, 101; teaching and learning strategies 130, 137–9, 140, 170, 171 Feingold, A. 43 Feldman, R. S. 82, 170 Feltovich, P. J. 122 Feme, M. C. 57 Festinger, L. 13–14, 168–9 Fink, L. D. 36 Finkenauer, C. 39 first impressions see impression formation Fischer-Lokou, J. 33, 34 Fiske, S. T. 39 Flanagan, E. 54 Fleming, J. 66 Flores, S. M. 51, 54 Foels, R. 7–8, 131 formative assessment 138, 146, 155 Fried, C. B. 82 friendships 12 Fritz, C. O. 33 Frymier, A. B. 38 fundamental attribution error 16, 18 Fung, T. K. 37 Fusani, D. S. 36 Gabelnick, F. 155 Gailliot, M. T. 15 Gaines, S. O. 57 Galea, S. 23 Gamoran, A. 52 Garcia, J. 84 Gardner, W. M. 35 gender, teacher-student relationship 38, 41 Georgia Southern University, Center for Excellence in Teaching 4 Gilbert, D. T. 74 Glass, G. V. 131 goals: behavioral outcome 36; and student autonomy 90, 91–2, 95, 96, 99

Goff, D. M. 21 Goff, P. A. 81 Goldney, R. D. 33 Gollwitzer, P. M. 135 Good, C. 82 Goossens, L. 101 Gordon, E. W. 55 Gorham, J. 37, 38 Graesser, A. C. 8, 118, 120 Graf, P. 9 Graham, E. E. 36 Grammar, K. 33 Greenberg, J. 144 Greene, M. J. 8 Greenidge, D. 57 Greenwald, A. G. 1, 14–15 Griffin, A. M. 39 Grigorenko, E. 110 Groopman, J. 87 group identity 55, 58, 59, 60 group orientation, and learning preferences 55, 58–67 group processes 12, 16, 20, 24, 32 Gueguen, N. 33, 34 Gumport, P. J. 51 Gurin, P. 52 Gurung, R. A. R. 2, 5–6, 7, 9, 17, 22, 23, 140, 161, 162, 163 H1N1 flu virus, example of critical thinking 117 Hacker, D. J. 8 Hackney, A. A. 163–5 Haddock, G. 150 Hakel, M. D. 9, 119 Hall, E. 136 Hall, G. Stanley 3 Hall, L. K. 9 Halonen, J. S. 144, 145, 146, 171 Halpern, D. F. 7, 9, 16, 116, 119, 127, 144, 148, 160, 168–70, 171 Hamm, D. 91 Hammer, E. D. 74, 166 Hammer, E. Y. 16 Hand, V. 58

Index Hardin, C. 39 Hardre, P. 96 Hargreaves, D. 53 Harris, M. J. 43 Hattie, J. A. C. 8, 21, 132, 133, 136, 137, 139, 140, 170–1 Hauert, C. 34 Haynie, A. 5–6 HBCUs: comparison with White universities 84–5, 86, 166; systematic study 76–85 health: application of social psychology 12; and social connectivity 34, 36 Hearn, K. D. 57 Hedges, L. 131 Heimann, R. 56 Heine, S. J. 53 Helms, J. E. 85 help-seeking attitudes and behavior 41, 104–13, 167–8; prospective study 106–11; recommendations for educators 111–12 helping behavior 12, 20 Henderson, M. 55 Henrich, J. 53 Henslee, A. M. 40 Herbart, Johann Friedrich 3 Hernandez, A. C. R. 168 Hertenstein, M. J. 34 Heussner, K. 126 Hickey, D. T. 101 Hill, G. W., IV 144 Holdsworth, L. 110 Holmes, R. M. 34 Hoss, R. A. 39 Hounsell, D. 5 Housley, M. K. 33 Hovland, C. L. 151 Howard, C. 54 Howard, D. C. 9 Hu, S. 146 Huang, L. 144 Hubbarde, A. 23 Huber, M. T. 6, 7

179

Hugenberg, K. 33 Hullett, C. R. 154 Hunter, L. 57 Hurd, S. N. 155 Hurley, E. A. 57, 165 Hurley, S. P. 165 Hutchings, P. 1, 4, 6–7 identity: group 55, 58, 59, 60; racial 58, 59, 85 Iizuka, Y. 55 immediacy 18, 37–8, 75 Implicit Association Test 14 impression formation 12, 15, 18, 19, 21–2, 38–42, 44, 163, 164 inclusion, recommendations of selfconstrual study 65–7 Inclusion of Others in the Self scale 61, 62, 64 independent/interdependent selfconstrual, and learning preference 54–67, 165–6 individual responsibility 16 individualism 55, 67, 165 information exchange 114–16, 126–7 Ing, M. 105 institutions see educational institutions International Alliance for Teaching Scholars (IATS) 4 International Journal for the Scholarship of Teaching and Learning (IJSOTL) 4, 6 International Society for the Scholarship of Teaching and Learning (ISSOTL) 4, 6 Internet 114–16, 126–7; journals 4, 5 intrinsic motivation 90, 91–2, 93, 95–6 Inzlicht, M. 82 Irons, J. G. 2 Iyengar, S. S. 55 Jaasma, M. A. 36–7 Jacobson, L. 43, 170 Jacobson, M. J. 122 Jagers, R. J. 57, 60 James, William 3

180

Index

Jang, H. 92, 93, 94, 96, 101 Janis, I. L. 151 Jeon, S. 93 Jewish Passover rite 3 Johnson, B. T. 150 Johnson, D. E. 6, 140 Johnson, D. W. 65–6 Johnson, R. T. 65–6 Johnson, S. 110 Journal of Chemical Education 5 Journal of College Science Teaching 5 Journal of Social and Clinical Psychology 16, 160, 161 Julka, D. L. 110 Kanat-Maymon, Y. 93, 94 Kang, S. H. 9 Kaplan, H. 92, 93, 94 Karabenick, S. A. 104, 105, 111 Karpicke, J. D. 9 Keller’s personalized system of instruction 134 Kelley, H. H. 151 Kelman, H. C. 152 Kendall, J. S. 118 Kerestes, A. M. 34 Kerns, N. 6 Kersting, N. 105 Kim, A. 94 King, E. B. 42 Kitayama, S. 56 Kleinke, C. L. 34 Klettke, B. 120 Kluger, A. N. 124, 137 Knapp, J. R. 104, 105 Knowles, M. L. 35 Kobyashyi 136 Koper, R. J. 37 Koriat, A. 8 Kornell, N. 8 Kraak, A. 52 Krauss, R. M. 147 Krieger, L. 94 Krull, D. S. 74 Kuh, G. D. 3, 146

Kulik, C.-L. C. 136 Kulik, J. A. 136 Kurlaender, M. 51, 54 Kwate, N. O. A. 57 La Guardia, J. G. 94 Lancaster, J. S. 166 Lane, L. T. 149 Langer, E. 22 Langlois, J. H. 39 language: teaching to support students’ autonomy 93, 97–8, 101; see also verbal cues Latham, G. P. 135 Lau, S. 34 Lavery, L. 136 law, application of social psychology to 12 Lawrence, N. K. 140 learned helplessness 18 learning: application of social psychology 1–2, 9–10, 13–14, 15–24, 160–73; and assessment 150; helpseeking attitudes and behavior 110–12; science of teaching 114–29, 168–70; teacher-student relationship 37, 44 learning activities 122–6 Learning Contexts Scenarios (LCS-C) 61, 62 learning epistemology 126 learning intentions, teaching strategies 130, 134–5, 170 learning objectives 118–19, 169–70, 171 learning perspectives 38, 130, 135–6, 139, 140 learning preferences: challenge 95–6; and self-construal 58–67, 165 learning strategies 130–42, 170–1; clear learning intentions and success criteria 130, 134–5, 137, 139; feedback 130, 137–9, 140; multiple teaching strategies to emphasise students’ learning perspectives 135–6, 139, 140 learning styles 3, 136

Index Leary, M. R. 32, 33, 34 Legg, A. M. 41, 164 Lens, W. 94 Lepper, M. R. 55 Letseka, M. 57 Leventhal, L. 131 Levy, S. R. 126 Lewin, Kurt 15 Li, H. Z. 61 Liberman, A. 151 Lieberman, E. 34 Lilly Foundation 4 Lin, M. 39 Lipko, A. R. 8 Lira, B. B. 33 Lockard, J. S. 34 Locke, E. A. 135 Lomotey, K. 53, 54 Loo, C. 54 Lopez, G. 52 Loscocco, K. A. 34 Louw, L. 57 Lowman, J. 37 Lucas, G. M. 35 Lundberg, C. A. 146 Lyons, P. M. 39 McCann, L. I. 40 McCarthy, M. A. 144, 146, 171 McClendon, S. A. 54, 65 McCusker, G. 55 McDaniel, D. 120 McDaniel, M. A. 9 McDermott, K. B. 9, 118, 120 McDowell, J. E. 33 McGaw, B. 131 McGhee, D. E. 1 McGlone, M. S. 81 MacGregor, J. 155 Machell, D. F. 36 McKeachie, W. J. 40 Mackie, D. M. 33 McKinney, K. 6 Macrae, C. N. 74 Mahabeer, M. 57

181

Maio, G. R. 150, 156 Maki, P. L. 145 Manly, J. B. 94 Markus, H. R. 56 Marryshow, D. 57, 61 Marsh, H. W. 140 Marx, D. M. 81 Marzano, R. J. 118 Mashek, D. J. 58, 61, 167–8 Mason, M. F. 74 Masterson, J. T. 38 mastery learning 134 Matthews, R. S. 155 Matthies, B. 57 Mattis, J. S. 57 Mayer, R. E. 123–4 Mehrabian, A. 34 Mehrotra, C. 145 Mehu, M. 33 memory 12, 118, 119 mental health, first impressions 39 Mentkowski, M. 145 Menzel, K. E. 38 mere exposure effect 19 Merwin, M. M. 36 Mestre, J. P. 116 metacognition 8, 125; strategies to enhance learning 130, 135–6, 138, 140 Metcalfe, J. 8, 9 Meyers, S. A. 10 Midgley, C. 104 Milgram, S. 23, 167 Miller, A. G. 39 Miller, C. H. 149 Miller, E. 52 Modigliani, A. 55 Moemeka, A. A. 55, 57 Molden, D. C. 35 Moller, A. C. 94 Monteith, M. J. 82 Moore, A. 38 moral reasoning 57 Morris, P. E. 33 Morrisette, N. 9 Moseley, D. V. M. 136

182

Index

motivation 18, 19; learners’ epistemology 126; support for students’ autonomy 90–103, 167; teaching strategies 134; teacher-student relationship 35, 36, 37, 38, 40, 41, 44; see also cognitive dissonance Mpofu, D. 35 multicultural environment, creating 66 Muraven, M. 35, 79 Murray, H. 37 Muthukrishna, N. 8 Myers, D. G. 32, 169, 170 Myers, R. 168 Nadler, A. 104 Nagda, B. A. 52 Naidoo, J. C. 57 Nasir, N. 58 National Center on Postsecondary Teaching, Learning, and Assessment 4 Naufel, K. Z. 163–5 Nelson, T. O. 8 Nemer, K. M. 105 Neuberg, S. 39 New Directions for Teaching and Learning series 9 Newcomb, M. D. 168 Newell, A. 135 Newsome, M. 155 Nguyen, H. D. 81 Nisbett, R. E. 14 Nist, S. L. 136 Nix, G. A. 91, 94 Nobles, W. 55, 56, 57 nonverbal cues 33–4, 36, 37–8, 42, 43–4, 75–6 Norenzayan, A. 53 Norton, R. W. 38 Nowak, M. A. 34 Nuss, C. K. 35 Nussbaum, J. F. 38 Nuthall, G. 137 obedience 12, 20, 23, 55, 167 Oberman, P. 106

Ohtsuki, H. 34 Olde, B. 120 Olson, J. M. 150, 156 O’Meara, K. 4 Omura, M. 96 Otta, E. 33 overjustification effect 16 Packer, J. 155 Pajares, F. 106, 110 Palmer, P. 1, 23 Pan, D. 2 Parham, T. A. 85 Pashler, H. 8, 9 Pashler, P. 121 Patrick, H. 104 Patterson, M. L. 43 pedagogical research (PR) 1; and assessment 156; champions 4–6; contribution of cognitive psychology 8–10; current controversies 6–7; history 3–4; meaning 2–3; psychology as disciplinary example 7–8; relevance of social psychology 12, 13, 15; social psychology of teaching and learning 15–24, 160–3, 164, 169, 172; teaching strategies 130, 139–40 Peden, B. F. 7 peer group 35, 55, 61; feedback 137; help seeking 108 Pelham, B. W. 74 Perlman, B. 40 Perry, R. P. 131 personality psychology 11 persuasion 12, 16, 19; and assessment 150–4 Petty, R. E. 150, 151, 154 Pintrich, P. R. 104, 105 Pires, C. S. 33 Pittinsky, T. L. 81 Plato 3 Pollard, R. 168 Poole, D. 9 Pope, R. 66 Potts, K. A. 149

Index PR see pedagogical research prejudice 1, 12, 13, 14–15, 19, 39; experience of Henry Louis Gates 73–4; in cross-racial teaching 73–89, 166; students’ experiences of nonverbal behavior 75–6; study of students’ perspectives in HBCUs 76–85, 86 Price, V. 23 Priest, R. F. 32 Prieto, L. R. 10, 17 Prislin, R. 150 productivity, social connectivity 35, 36 Prohaska, T. 170 PsycINFO 164 Psychometric Analysis of Computer Science Help-Seeking Scales 106 Punnett, B. J. 57 Purdie, N. 136 Purdie-Vaughns, V. 84 Pygmalion Effect 18; see also self-fulfilling prophesy Quinones, M. A. 42, 124 Quintilian 3 Rabow, J. 168 race 14–15, 41; self-construal 52–72, 165; unintentional prejudice and crossracial teaching 73–89, 166 racial identity 58, 59, 85 racism and racial discrimination 54, 59–60, 61, 62, 63, 64, 65, 66, 75, 77, 85 Radosevich, D. J. 121 Ramkissoon, M. 57 Ramsey, J. 57 Ramsey, J. L. 39 rapport 18, 37–8, 40, 44, 164 reactance theory 143, 144, 146–9, 154, 155, 156, 171–2; consequences of reactance 147–9; response to academic change 149–50 reaction time 14–15 reciprocity theory 16

183

Reddick, R. 54 Reeve, J. 91, 92, 93, 94, 95, 96, 101, 155, 165, 167 Reis-Bergan, M. 146 relational teaching 36 religiosity 57 retrieval 9, 118, 119–20 Reynolds, A. 66 Rice, Joseph Mayer 3 Rice, R. E. 4 Rich, Adrienne 51 Richeson, J. A. 79–80 Richlin, L. 118, 155, 161–3 Ritts, V. 43 Rivers, W. 53, 54 Roediger, H. L., III 9, 118, 120 Rogers, Carl R. 100–1, 167 Rohrer, D. 9, 121 Rolison, G. 54 romantic relationships 12 Rosenhan, D. L. 39 Rosenman, S. J. 33 Rosenthal, R. 21, 42, 43, 170 Ross, L. D. 34, 83–4 Roth, G. 93, 94 Rothman, A. J. 39 Rowell, E. 38 Rowley, S. A. J. 85 Royer, M. J. 116 Rubenstein, A. J. 39 Rugett, J. K. 146 Rule, N. O. 39 Rumberger, R. W. 54 Ryan, A. M. 81, 104, 105, 111 Ryan, R. M. 90, 91, 92, 93, 94 Sabini, J. 22 Sadler, D. R. 138 Saegert, S. 33 Saville, B. K. 37, 164 Sawyer, J. 32 Schaeffer, G. 21, 164 Schaller, K. A. 36 Scheiner, M. J. 94 Schiff, T. W. 51, 53

184

Index

Schmidt, R. A. 125 scholarly teaching 2, 161–3, 172 scholarship of teaching and learning (SoTL) 1; and assessment 156; champions 4–6; contribution of cognitive psychology 8–10; current controversies 6–7; history 3–4; meaning 2–3; psychology as disciplinary example 7–8; relevance of social psychology 12, 13, 15; social psychology of teaching and learning 15–24, 160–3, 164, 169, 172; teaching strategies 130, 139–40 Schreiner, L. A. 146 Schwab, Joseph 3 Schwartz, A. 92 Schwartz, B. M. 2, 6, 7, 17, 21 Schwartz, J. 35 Schwartz, J. L. K. 1 Schwarz, N. 14 science of learning, application to art of teaching 114–29, 168–70 scientific method 13 scientist-educator model 10 self-concept 19, 22 self-construal 54–67, 165; in African diaspora 57; culture and 55–6, 58; definition 54–5; in higher education institutions 56–7; model and investigation of students in South Africa, US and Barbados 53, 57–65; recommendations 65–7 self-construal scale 60, 61–2 self-control see self-regulation self-determination theory (SDT) 90, 91, 166, 167 self-disclosure 34 self-efficacy 10, 22 self-esteem 19, 22 self-evaluation 136 self-fulfilling prophecy 19, 22, 42, 42–4, 75 self-handicapping 16, 19, 23 self-instruction 136 self-perception theory 168

self-regulation 15, 19, 35–6; cross-racial teaching 79–80, 82; strategies for teaching and learning 135 self-serving bias 16, 19, 145 Sellers, R. M. 85 Senior, A. M. 62 Shao, L. P. 155 Shapiro, J. R. 42 Sharkey, W. 54, 60 Shavelson, R. J. 144 Shavit, Y. 52 Shea, K. A. 38 Sheer, V. C. 37 Sheeran, P. 135 Sheinman, L. 92 Sheldon, K. M. 94 Shelton, J. N. 79–80, 85 Sherman, J. G. 134 Sherman, J. W. 82 Shih, M. 81 Shim, S. 104 Shulman, L. 4 Shwalb, B. J. 136 Sierens, E. 101 Sigler, E. 43 Sikorski, J. 164 Simon, D. A. 122 Simons, J. 94 Simpson, M. L. 136 Singelis, T. M. 54, 60, 61, 62 Skinner, E. A. 94 Skoner, D. P. 34 Slamecka, N. J. 9 small talk 33, 36 smiling 33–4, 36, 42 Smith, B. L. 155 Smith, C. 155 Smith, M. A. 85 Smith, M. L. 131 Smith, R. 2, 7, 9, 16, 17 Smith, W. A. 53, 54 Smollan, D. 61 Snyder, C. P. 10, 16, 160, 161, 162 Snyder, M. 42 social categorization 16

Index social cognition 10, 18; see also selfefficacy social comparison 12, 16, 18, 23 social connectivity 32–8, 44, 163–4, 171; cues for positive social interactions 33–4; effects 34–6; relevance to teaching and learning 36–8 social facilitation 11, 20 social inhibition 20 social loafing 20, 24 social norms 16, 23 social psychology: definition 10–11, 145; history 11–12; levels of analysis 12; methods 12–15; relevance to assessment 145, 150–6, 171; relevance to teaching and learning 1, 9–10, 15–24, 160–73 social rejection 34–6 social support 12, 35, 55; and help seeking 109, 110 Society for the Psychological Study of Social Issues 15 Society for the Teaching of Psychology 7 sociology 11 Soenens, B. 101 Solmon, L. C. 51, 53 Solmon, M. S. 51, 53 Sommer, K. L. 35 SoTL see scholarship of teaching and learning Son, L. 8 Spielmans, G. I. 110 Spiro, R. J. 122 Spitze, G. 34 spotlight effect 18 Stadler, J. R. 21 Stahl, J. M. 166 Stanny, C. J. 145 Steele, C. M. 74, 81, 83–4 Stephenson, C. L. 168–70, 171 stereotype threat 81–2, 166 stereotypes 13, 14–15, 19, 39, 41, 166; cross-racial teaching 73, 74, 75, 81–2, 87, 167 Stoloff, M. L. 146

185

stranger anxiety 32–3 Stroessner, S. J. 126 Stroop task 80 student autonomy: benefits 93–4, 101; help seeking 110–11, 112; involvement of cross-racial students in research 166–7; and learning objectives 169–70, 171; and selfconstrual 165–6; support in teaching 90–103, 171 student-centred learning 16, 168 student learning community programs 155 student-regulated learning 130, 135–6, 140 students: achievement 40–1, 54, 59, 82, 86, 104–13, 130–3, 170–1; changing profile 52–3; curiosity and preference for challenge 95–6; diversity 51–72, 165; epistemology 126; expectations 164–5, 170; experiences of unintentional prejudice and cross-racial teaching 75–87; goals and values 90, 91–2, 95, 96, 99; help seeking and achievement 104–13, 168; metacognition 125, 130, 135–6, 138, 140; negative affect 99–100, 101, 111; responses 119–20; see also attitudes; learning; motivation; student autonomy; teacher-student relationship Su, Y. 94 summative assessment 146, 155 Sundre, D. 146 Swadi, H. 35 Swann, W. B., Jr. 42 Swap, W. 33 Syme, S. 23 Szpunar, K. K. 118 Tajfel, H. 32, 84 Tangney, J. P. 61 Tanke, E. D. 42 Taraban, R. 168 teacher-student relationship 12, 16, 21–3, 32–50, 163–4; cross-racial teaching 85–7

186

Index

teaching: application of science of learning to 114–29, 168–70; application of social psychology 1–2, 9–10, 15–24, 160–73; and critical thinking 87, 116–27, 168–70; cross-racial situation 73–89, 166; key dilemma 100–1; motivating style 91, 92–3; patience 93, 98–9, 101; recommendations of self-construal research 65–6; supporting student autonomy 90–103, 167; see also faculty; teaching strategies teaching assistant (TA) training literature 10 teaching burnout 36 Teaching of Psychology 5, 6, 7 Teaching Professor 4 teaching strategies 8, 130–42, 170–1; communicating clear learning intentions and success criteria 134–5, 137, 139; feedback 137–9, 140; multiple strategies to emphasise students’ learning perspectives 135–6, 139, 140 Terrell, F. 61 Terrell, S. 61 test enhanced learning 119–20 testing see assessment Teven, J. J. 36 Thaver, B. 54 Thelin, J. R. 56 Tice, D. M. 35, 79 Tidd, K. L. 33 Timperley, H. 137 Tjosvold, D. 147, 148 Todorov, A. 39 Tomcho, T. J. 7–8, 131 Tormala, Z. L. 150 touch 33, 34, 36, 38, 42 Trandis, H. 55, 56 Triplett, N. 11 Triplette, B. 9 trust, trustworthiness 34, 36–7, 38, 84, 152; cultural mistrust 59, 60, 61, 62, 63, 64, 65

Tubbs, M. E. 43 Tuitt, F. 54 Turner, J. C. 84 Turner, R. 34 Twale, D. J. 149 Twenge, J. M. 35, 36 Tyler, K. 57 Ubuntu 57 Umbach, P. D. 146 van Beest, I. 34–5 Vansteenkiste, M. 94, 101 verbal cues 33, 36, 37 Verkamp, J. M. 34 Verme, P. 110 Vespia, K. M. 22 Vives, Juan Luis 3 Vohs, K. D. 39 volunteerism 57 Vul, E. 8 Walster, E. 39 Wawrzynski, M. R. 146 Webb, N. M. 105 Weeden, J. 22 Weimer, M. 4, 5, 6 West, R. 36 Westman, A. S. 33 Wetzel. C. G. 34 Wheeler, S. C. 150 Wheeless, L. R. 38 Whitla, D. 54 Wicklund, R. A. 150 Williams, G. C. 94 Williams, K. D. 34–5 Willis, J. 39 Wilson, J. H. 21, 38, 40, 41, 163–5, 170, 171 Wilson, S. B. 40 Wilson VanVoorhis, C. R. 7 Winstein, C. J. 125 Witt, P. L. 38 Wixted, J. T. 9

Index Wojcieszak, M. 23 Woods, L. N. 57 Word, C. O. 42, 75 World War II 12 World Wide Web see Internet Wray, R. 116 Wright, R. A. 144 Yamamura, T. 167–8

Ybarra, O. 35 Young, A. M. 149 Zajonc, R. B. 33 Zanna, M. P. 42, 75, 150 Zarow, G. 9 Zimbardo, P 1 Zinn, T. E. 21, 164 Zubizarreta, J. 145

187