Dissecting Knowledge, Guessing, and Blunder in Multiple Choice Assessments
Bibliographic record
Abstract
Multiple choice results are inherently probabilistic outcomes, as correct responses reflect a combination of knowledge and guessing, while incorrect responses additionally reflect blunder, a confidently committed mistake. To objectively resolve knowledge from responses in an MC test structure, we evaluated probabilistic models that explicitly account for guessing, knowledge and blunder using eight assessments (>9,000 responses) from an undergraduate biotechnology curriculum. A Bayesian implementation of the models, aimed at assessing their robustness to prior beliefs in examinee knowledge, showed that explicit estimators of knowledge are markedly sensitive to prior beliefs with scores as sole input. To overcome this limitation, we examined self-ranked confidence as a proxy knowledge indicator. For our test set, three levels of confidence resolved test performance. Responses rated as least confident were correct more frequently than expected from random selection, reflecting partial knowledge, but were balanced by blunder among the most confident responses. By translating evidence-based guessing and blunder rates to pass marks that statistically qualify a desired level of examinee knowledge, our approach finds practical utility in test analysis and design.
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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 | 0.431 | 0.781 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".