The effect of research-based instruction in introductory physics on a common cognitive bias
Bibliographic record
Abstract
Inspired by a paper at last year's PERC conference, in which Rebello [1] compared students' individual and cohort mean score estimations with their actual assessment scores, we present results of a study in which students in an introductory physics class were asked to predict their scores on two assessments, one delivered at the start of the course (pre-instruction) and one at the end of the course (post-instruction). Our results show that, pre-instruction, the academically strongest students tend to underestimate their score slightly, whereas the weakest overestimate their performance significantly, consistent with the findings of Rebello and demonstrating a well-known cognitive bias (the Dunning-Kruger effect). Post-instruction, we find that the ability of the original weakest quartile cohort to accurately predict their own assessment score has improved significantly, but a flux of students between quartiles from one assessment to the other reveals that the least able students continue to over-estimate their performance, but with a reduced mean discrepancy. We discuss the implications these results have for instruction and for development of enhanced metacognition amongst physics students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".