Even after thirteen class exams, students are still overconfident: the role of memory for past exam performance in student predictions

Students often are overconfident when they predict their performance on classroom examinations, and their accuracy often does not improve across exams. One contributor to overconfidence may be that students did not have enough experience, and another is that students may under-use their knowledge of prior exam performance to predict performance on their upcoming exams. To evaluate the former, we examined student prediction accuracy across 13 exams in an introductory course on educational psychology. For the latter, we computed measures that estimate the extent to which students use the prior exam score when predicting performance and whether students should use the prior exam scores. Several outcomes are noteworthy. First, students were overconfident, and contrary to expectations, this overconfidence did not decline across exams. Second, students’ prior exam scores were not related to subsequent predictions, even though prior exam performance showed little bias with respect to predicting future performance. Thus, students appear to under-use prior performance despite its utility for improving prediction accuracy about future exam performance.

[1]  Linda Bol,et al.  Explaining calibration accuracy in classroom contexts: the effects of incentives, reflection, and explanatory style , 2008 .

[2]  Douglas J. Hacker,et al.  A Comparison of the Effects of Practice Tests and Traditional Review on Performance and Calibration , 2001 .

[3]  Nancy J. Stone,et al.  Exploring the Relationship between Calibration and Self-Regulated Learning , 2000 .

[4]  Philip H. Winne,et al.  Self-regulated learning viewed from models of information processing. , 2001 .

[5]  Lisa Geraci,et al.  Training metacognition in the classroom: the influence of incentives and feedback on exam predictions , 2011 .

[6]  J. Shaughnessy,et al.  Confidence-Judgment Accuracy as a Predictor of Test Performance. , 1979 .

[7]  John Dunlosky,et al.  Improving Students’ Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology , 2012 .

[8]  Janet Metcalfe,et al.  The role of memory for past test in the underconfidence with practice effect. , 2007, Journal of experimental psychology. Learning, memory, and cognition.

[9]  Linda Bol,et al.  Test prediction and performance in a classroom context. , 2000 .

[10]  Eric Luis Uhlmann,et al.  Wishful Thinking , 2011, Psychological science.

[11]  M. E. Gredler,et al.  An Exploratory Study of Academic Goal Setting, Achievement Calibration and Self-Regulated Learning , 2002 .

[12]  Judy Kay,et al.  Coming to terms with Bloom: an online tutorial for teachers of programming fundamentals , 2012, ACE 2012.

[13]  J. Dunlosky,et al.  The sensitivity of judgment-of-learning resolution to past test performance, new learning, and forgetting , 2011, Memory & cognition.

[14]  Sabina Kleitman,et al.  The role of a novel formative assessment tool (Stats-mIQ) and individual differences in real-life academic performance☆ , 2014 .

[15]  C Hertzog,et al.  Relationships between metamemory, memory predictions, and memory task performance in adults. , 1990, Psychology and aging.

[16]  Li Cao,et al.  The effect of distributed monitoring exercises and feedback on performance, monitoring accuracy, and self-efficacy , 2006 .

[17]  Patrick J Curran,et al.  Twelve Frequently Asked Questions About Growth Curve Modeling , 2010, Journal of cognition and development : official journal of the Cognitive Development Society.

[18]  R. Mayer,et al.  Interactive Multimodal Learning Environments , 2007 .

[19]  W. K. Campbell,et al.  Self-Threat Magnifies the Self-Serving Bias: A Meta-Analytic Integration , 1999 .

[20]  John Dunlosky,et al.  Overconfidence produces underachievement: Inaccurate self evaluations undermine students’ learning and retention , 2012 .

[21]  Christopher Hertzog,et al.  Metamemory and aging: Relations between predicted, actual and perceived memory task performance , 1994 .

[22]  Michael J. Serra,et al.  People use the memory for past-test heuristic as an explicit cue for judgments of learning , 2014, Memory & cognition.

[23]  I. Bajsanski,et al.  Metacognitive Judgments and Syllogistic Reasoning , 2014 .

[24]  Janet Metcalfe,et al.  Judgments of Learning are Influenced by Memory for Past Test. , 2008, Journal of memory and language.

[25]  P. Ditto,et al.  Motivated Skepticism: Use of Differential Decision Criteria for Preferred and Nonpreferred Conclusions , 1992 .

[26]  Eugene B. Zechmeister,et al.  Judgments of knowing: The influence of retrieval practice. , 1980 .

[27]  Paul W. Grimes,et al.  The Overconfident Principles of Economics Student: An Examination of a Metacognitive Skill , 2002 .

[28]  Fiona Steele,et al.  Multilevel models for longitudinal data , 2007 .

[29]  P. Winne,et al.  Feedback and Self-Regulated Learning: A Theoretical Synthesis , 1995 .

[30]  Judy Kay,et al.  Over-confidence and confusion in using bloom for programming fundamentals assessment , 2012, SIGCSE '12.

[31]  Matthew G. Rhodes,et al.  Multiple bases for young and older adults' judgments of learning in multitrial learning. , 2012, Psychology and aging.

[32]  Douglas J. Hacker,et al.  The Influence of Overt Practice, Achievement Level, and Explanatory Style on Calibration Accuracy and Performance , 2005 .

[33]  J. R. Larson,et al.  Evidence for a self‐serving bias in the attribution of causality1 , 1977 .

[34]  L. Abramson,et al.  Is there a universal positivity bias in attributions? A meta-analytic review of individual, developmental, and cultural differences in the self-serving attributional bias. , 2004, Psychological bulletin.

[35]  K. Thiede.  The importance of monitoring and self-regulation during multitrial learning , 1999, Psychonomic bulletin & review.

[36]  J. Dunlosky,et al.  Updating knowledge about encoding strategies: a componential analysis of learning about strategy effectiveness from task experience. , 2000, Psychology and aging.