The Attributions of Students’ Confidence Judgments and Related Feedback
Bibliographic record
Abstract
Much research has demonstrated that low performers tend to be prone to overconfidence, while high performers are disposed to underconfidence. Still, students’ attributions for their confidence judgements and how their judgements relate to academic attitudes, such as feedback preferences, remains undetermined. Undergraduate students in eight introductory psychology classes made confidence judgements for their psychology midterm exam, then reported their attributions for the estimate. One week later, students received their exam score back, assessed how their actual performance compared to their expectations and ranked their feedback preferences. Consistent with past work, low performers were overconfident and high performers were slightly underconfident. Overconfident students made significantly more internal and external attributions than underconfident students. The most influential attributions for both groups were the perceived difficulty and relevancy of exam questions. Additionally, a significant negative relationship between confidence judgement bias and feedback preferences suggests that as students become underconfident their preference for fewer feedback increases. These results indicate that overconfident learners are more motivated to provide explanations for their confidence judgements, possibly due to cognitive dissonance between their expected ability and actual ability. Contrary to expectations, overconfidence did not have a relationship with maladaptive feedback preferences. Future work would benefit from using alternative methodologies, such as using open-ended questions or a think-aloud protocol.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".