Abstract
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.
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Notes
As we detail in the discussion, such between-participant correlational analyses are indirect indicators of individual student’s judgment accuracy; so, this correlational evidence from Hacker et al. (2000) does not provide definitive support that MPE was being used by any given student.
The weighted mean bias was calculated because the N for each exam was different as a result of some students missing some of the exams. Thus, the weighted mean bias was computed by multiplying the mean bias score for each exam by the N for that exam, summing the products together, and dividing this sum by the total N.
To be included in the ANOVA, a student must have completed all 13 exams. Because students were allowed to drop one exam score from their overall course grade, many were missing at least one exam score. Multilevel models can be estimated when data are partially missing (Curran et al. 2010) whereas structural equations require some method to account for missing data, such as multiple imputation (Steele 2008).
We thank an anonymous reviewer for suggesting this analysis.
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Foster, N.L., Was, C.A., Dunlosky, J. et al. Even after thirteen class exams, students are still overconfident: the role of memory for past exam performance in student predictions. Metacognition Learning 12, 1–19 (2017). https://doi.org/10.1007/s11409-016-9158-6
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DOI: https://doi.org/10.1007/s11409-016-9158-6