Changes in implicit theories of ability in biology and dropout from STEM majors: A latent growth curve approach

This longitudinal study was designed to investigate the associations between changes in implicit theories of ability in biology and college students’ dropout from STEM majors. We modeled the one-year growth patterns of entity and incremental beliefs about ability in biology with 4 time points of self-reported data and two covariates—biology domain knowledge and inference making and gateway course grade, and predicted STEM dropout with the growth trajectories of implicit theories. Results indicated that students’ entity beliefs increased, while incremental beliefs decreased over time, which provides support for the changeability of implicit beliefs over a short period of time. The growth of incremental beliefs was directly associated with STEM dropout above and beyond biology course grade and biology domain knowledge and inference making. Low intercept and negative slope of incremental beliefs predicted leaving STEM majors; however, the decline of entity beliefs did not have significant effects on dropout. Interestingly, the effect of biology domain knowledge and inference making on STEM dropout was mediated by biology course grade and incremental beliefs. The findings imply the importance of monitoring changes in students’ implicit beliefs and gateway course achievement in order to better understand and promote STEM retention.

[1]  C. Dweck,et al.  Mindsets and human nature: promoting change in the Middle East, the schoolyard, the racial divide, and willpower. , 2012, The American psychologist.

[2]  Jason A. Chen.  Implicit Theories, Epistemic Beliefs, and Science Motivation: A Person-Centered Approach , 2012 .

[3]  K. Stecher,et al.  Unexpected improvement, decline, and stasis: a prediction confidence perspective on achievement success and failure. , 2007, Journal of personality and social psychology.

[4]  Jessica Gore.  The Importance of Freshman Experiences in Predicting Students’ Retention Decisions , 2011 .

[5]  Amy M. Hightower,et al.  Science and Engineering Indicators , 1993 .

[6]  H. Kappes,et al.  Implicit theories moderate the relation of positive future fantasies to academic outcomes , 2011 .

[7]  B. Byrne.  Structural equation modeling with EQS : basic concepts, applications, and programming , 2000 .

[8]  Frank M. Pajares,et al.  Implicit Theories of Ability of Grade 6 Science Students: Relation to Epistemological Beliefs and Academic Motivation and Achievement in Science. , 2010 .

[9]  Adam V. Maltese,et al.  Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among U.S. students , 2011 .

[10]  William J. Hussar,et al.  The Condition of Education , 2010 .

[11]  D. Flora,et al.  An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. , 2004, Psychological methods.

[12]  Shu-shen Shih.  Perfectionism, Implicit Theories of Intelligence, and Taiwanese Eighth-Grade Students’ Academic Engagement , 2011 .

[13]  Craig K. Enders,et al.  Missing Data in Educational Research: A Review of Reporting Practices and Suggestions for Improvement , 2004 .

[14]  Ivar Bråten,et al.  The Relationship between Motivational Beliefs and Learning Strategy Use among Norwegian College Students , 1998, Contemporary educational psychology.

[15]  Jennifer G. Cromley,et al.  The role of identity development, values, and costs in college STEM retention , 2014 .

[16]  M. J. Chang,et al.  From Gatekeeping to Engagement: A Multicontextual, Mixed Method Study of Student Academic Engagement in Introductory STEM Courses , 2011, Research in higher education.

[17]  P. Gore.  Academic Self-Efficacy as a Predictor of College Outcomes: Two Incremental Validity Studies , 2006 .

[18]  E. Gonida,et al.  Implicit theories of intelligence, perceived academic competence, and school achievement: testing alternative models. , 2006, The American journal of psychology.

[19]  Carrie B. Fried,et al.  Reducing the Effects of Stereotype Threat on African American College Students by Shaping Theories of Intelligence , 2002 .

[20]  Vincent Tinto,et al.  From Theory to Action: Exploring the Institutional Conditions for Student Retention , 2010 .

[21]  M. Dresel.  A Longitudinal Analysis of Dweck's Motivation-Process-Model in the Classroom , 2001 .

[22]  T. Lund,et al.  Academic Self‐concept, Implicit Theories of Ability, and Self‐regulation Strategies , 2005 .

[23]  E. Pomerantz,et al.  Mothers' daily person and process praise: implications for children's theory of intelligence and motivation. , 2013, Developmental psychology.

[24]  Karen Buro,et al.  Implicit beliefs, achievement goals, and procrastination: A mediational analysis , 2009 .

[25]  J. Singer,et al.  Applied Longitudinal Data Analysis , 2003 .

[26]  B. Christe.  The Importance of Faculty-Student Connections in STEM Disciplines: A Literature Review , 2013 .

[27]  S. M. Wischusen,et al.  Impact of a Short Pre-Freshman Program on Retention , 2011 .

[28]  Susan Goldin-Meadow,et al.  Parent praise to 1- to 3-year-olds predicts children's motivational frameworks 5 years later. , 2013, Child development.

[29]  C. Dweck,et al.  Self-Th eories and Motivation: Students’ Beliefs About Intelligence , 2009 .

[30]  Joshua Aronson,et al.  Improving adolescents' standardized test performance: An intervention to reduce the effects of stereotype threat , 2003 .

[31]  S. Kornilov,et al.  Subjective evaluations of intelligence and academic self-concept predict academic achievement: Evidence from a selective student population , 2009 .

[32]  D. Stipek,et al.  Children's beliefs about intelligence and school performance. , 1996 .

[33]  B. Muthén,et al.  Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm , 1999, Biometrics.

[34]  Trudy Howles,et al.  A study of attrition and the use of student learning communities in the computer science introductory programming sequence , 2009, Comput. Sci. Educ..

[35]  Lisa S. Blackwell,et al.  Implicit theories of intelligence predict achievement across an adolescent transition: a longitudinal study and an intervention. , 2007, Child development.

[36]  J. Schafer,et al.  Missing data: our view of the state of the art. , 2002, Psychological methods.

[37]  Peter A. Daempfle.  An Analysis of the High Attrition Rates among First Year College Science, Math, and Engineering Majors , 2003 .

[38]  Andrew J. Martin,et al.  Adaptability: How students’ responses to uncertainty and novelty predict their academic and non-academic outcomes. , 2013 .

[39]  A. Elliot,et al.  Implicit theories and IQ test performance: A sequential mediational analysis ☆ , 2008 .

[40]  Eric M. VanEpps,et al.  Mind-sets matter: a meta-analytic review of implicit theories and self-regulation. , 2013, Psychological bulletin.

[41]  Timothy P. Scott,et al.  Predicting Retention of Mathematics and Science Majors at a Research One Institution and Suggested Advising Tools. , 2009 .

[42]  Marylène Gagné,et al.  Pursuing academic interests: The role of implicit theories. , 2001 .

[43]  A. Davey,et al.  Statistical Power Analysis with Missing Data: A Structural Equation Modeling Approach , 2009 .

[44]  D. Lubinski,et al.  Spatial ability for STEM domains: Aligning over 50 years of cumulative psychological knowledge solidifies its importance. , 2009 .

[45]  Cs Dweck,et al.  Messages that motivate: How praise molds students’ beliefs, motivation, and performance (in surprising ways). Aronson (Ed.), Improving academic achievement: Impact of psychological factors on education (pp. ). Amsterdam: Academic Press. , 2002 .

[46]  Anton E. Lawson,et al.  Self-efficacy, reasoning ability, and achievement in college biology , 2007 .

[47]  B. Packard.  Mentoring and Retention in College Science: Reflections on the Sophomore Year , 2004 .

[48]  Lei Bao,et al.  Addressing STEM Retention through a Scientific Thought and Methods Course. , 2012 .

[49]  Emily J. Shaw,et al.  Patterns of Persistence in Intended College Major with a Focus on STEM Majors. , 2010 .

[50]  Dennis M. McInerney,et al.  How you think about your intelligence determines how you feel in school: The role of theories of intelligence on academic emotions , 2012 .

[51]  Yi Pan,et al.  Exploring relations among college students' prior knowledge, implicit theories of intelligence, and self-regulated learning in a hypermedia environment , 2010, Comput. Educ..

[52]  Y. Stephan,et al.  Coping with examinations: exploring relationships between students' coping strategies, implicit theories of ability, and perceived control. , 2009, The British journal of educational psychology.

[53]  Phillip L. Ackerman,et al.  Trait complex, cognitive ability, and domain knowledge predictors of baccalaureate success, STEM persistence, and gender differences. , 2013 .

[54]  Birgit Spinath,et al.  Implicit theories about personality and intelligence and their relationship to actual personality and intelligence , 2003 .

[55]  Gregory R. Hancock,et al.  The Reliability Paradox in Assessing Structural Relations Within Covariance Structure Models , 2011 .

[56]  J. H. Steiger,et al.  Accomplishment in Science, Technology, Engineering, and Mathematics (STEM) and Its Relation to STEM Educational Dose: A 25-Year Longitudinal Study , 2010 .

[57]  G. Marshall,et al.  Against the Odds , 1999 .

[58]  C. Dweck.  Self-theories and lessons for giftedness: a reflective conversation , 2008 .

[59]  Ivar Bråten,et al.  The relationship between epistemological beliefs, implicit theories of intelligence, and self-regulated learning among Norwegian postsecondary students. , 2005, The British journal of educational psychology.

[60]  Carol S. Dweck,et al.  Self-Theories: Their impact on competence motivation and acquisition , 2005 .

[61]  C. Dweck.  Self-Theories: Their Role in Motivation, Personality, and Development. Essays in Social Psychology. , 1999 .

[62]  Carol S. Dweck,et al.  Self-Theories Motivate Self-Regulated Learning , 2012 .

[63]  C. Mariné,et al.  Implicit Theories of Intelligence, Goal Orientation, Cognitive Engagement, and Achievement: A Test of Dweck's Model with Returning to School Adults. , 2005 .

[64]  K. Rask.  Attrition in STEM Fields at a Liberal Arts College: The Importance of Grades and Pre-Collegiate Preferences , 2010 .

[65]  Carol S. Dweck,et al.  The Development of Ability Conceptions , 2002 .

[66]  S. Schwartz,et al.  Leaving College: Rethinking the Causes and Cures of Student Attrition , 1987 .

[67]  S. Kurpius,et al.  Promoting Academic Persistence Among Racial/Ethnic Minority and European American Freshman and Sophomore Undergraduates: Implications for College Counselors , 2013 .

[68]  Paul R. Hernandez,et al.  Sustaining Optimal Motivation: A Longitudinal Analysis of Interventions to Broaden Participation of Underrepresented Students in STEM. , 2013, Journal of educational psychology.

[69]  J. Graham,et al.  Missing data analysis: making it work in the real world. , 2009, Annual review of psychology.

[70]  Carey S. Ryan,et al.  Longitudinal changes in college math students’ implicit theories of intelligence , 2013 .

[71]  I. Warner,et al.  Hierarchical Mentoring: A Transformative Strategy for Improving Diversity and Retention in Undergraduate STEM Disciplines , 2011, Journal of Science Education and Technology.

[72]  V. Pandey.  Intelligence and Motivation , 2005 .

[73]  L. Angeles.  Evaluating Cutoff Criteria of Model Fit Indices for Latent Variable Models with Binary and Continuous Outcomes , 2002 .

[74]  Ellen L. Usher,et al.  Profiles of the Sources of Science Self-Efficacy. , 2013 .

[75]  Del Siegle,et al.  Exploring the Relationship of College Freshmen Honors Students’ Effort and Ability Attribution, Interest, and Implicit Theory of Intelligence With Perceived Ability , 2010 .

[76]  M. Villarejo,et al.  Importance of Undergraduate Research for Minority Persistence and Achievement in Biology , 2010 .

[77]  C. Dweck,et al.  Implicit Theories: Elaboration and Extension of the Model , 1995 .

[78]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[79]  Qian Wang,et al.  Chinese students’ implicit theories of intelligence and school performance: Implications for their approach to schoolwork , 2012 .

[80]  E. Seymour,et al.  Talking About Leaving: Why Undergraduates Leave The Sciences , 1997 .

[81]  C. Dweck,et al.  Implicit Theories and Their Role in Judgments and Reactions: A Word From Two Perspectives , 1995 .

[82]  B. Muthén,et al.  Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes , 1997 .

[83]  Jody L. Davis,et al.  Against the odds: academic underdogs benefit from incremental theories , 2011 .

[84]  W. Gardner,et al.  Handbook of motivation science , 2008 .

[85]  K. Burns,et al.  Promoting malleability is not one size fits all: Priming implicit theories of intelligence as a function of self-theories , 2007 .

[86]  J. Shea.  National Science Education Standards , 1995 .

[87]  Vincent Tinto,et al.  Leaving College: Rethinking the Causes and Cures of Student Attrition. , 1988 .

[88]  Thomas D. Snyder,et al.  The Condition of Education 2009. NCES 2009-081. , 2009 .

[89]  D. Rubin.  INFERENCE AND MISSING DATA , 1975 .

[90]  David F. Feldon,et al.  Translating Expertise into Effective Instruction: The Impacts of Cognitive Task Analysis (CTA) on Lab Report Quality and Student Retention in the Biological Sciences. , 2010 .