Are faculty predictions or item taxonomies useful for estimating the outcome of multiple-choice examinations?
Bibliographic record
Abstract
The purpose of this study was to evaluate whether multiple-choice item difficulty could be predicted either by a subjective judgment by the question author or by applying a learning taxonomy to the items. Eight physiology faculty members teaching an upper-level undergraduate human physiology course consented to participate in the study. The faculty members annotated questions before exams with the descriptors "easy," "moderate," or "hard" and classified them according to whether they tested knowledge, comprehension, or application. Overall analysis showed a statistically significant, but relatively low, correlation between the intended item difficulty and actual student scores (ρ = -0.19, P < 0.01), indicating that, as intended item difficulty increased, the resulting student scores on items tended to decrease. Although this expected inverse relationship was detected, faculty members were correct only 48% of the time when estimating difficulty. There was also significant individual variation among faculty members in the ability to predict item difficulty (χ(2) = 16.84, P = 0.02). With regard to the cognitive level of items, no significant correlation was found between the item cognitive level and either actual student scores (ρ = -0.09, P = 0.14) or item discrimination (ρ = 0.05, P = 0.42). Despite the inability of faculty members to accurately predict item difficulty, the examinations were of high quality, as evidenced by reliability coefficients (Cronbach's α) of 0.70-0.92, the rejection of only 4 of 300 items in the postexamination review, and a mean item discrimination (point biserial) of 0.37. In conclusion, the effort of assigning annotations describing intended difficulty and cognitive levels to multiple-choice items is of doubtful value in terms of controlling examination difficulty. However, we also report that the process of annotating questions may enhance examination validity and can reveal aspects of the hidden curriculum.
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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 | 0.024 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".