Question format is the best predictor of item discrimination: a multivariable analysis
Bibliographic record
Abstract
ABSTRACT Item discrimination, the point-biserial correlation between performance on a question and total exam score, was analyzed in relation to question format, difficulty, and Bloom’s taxonomy, which are rarely studied together in a joint framework. To estimate their independent contributions in undergraduate biology examinations, simultaneous multivariable linear and logistic models were conducted on an upper year biology item bank ( n = 754). Predictors were question format (multiple-choice question [MCQ], short answer [SA], true/false [TF]), difficulty (easy >80%, moderate 60%–80%, hard <60%), and Bloom (recall, understanding, application, analysis). Question format was the strongest independent predictor. Relative to MCQs, SA items showed higher discrimination and greater odds of meeting the ≥0.35 threshold (odds ratio [OR] = 4.18), while TF items were less discriminating (OR = 0.58). Moderate and hard items exceeded easy questions (ORs = 2.75 and 2.45, respectively). For Bloom, higher-order items outperformed recall questions overall; both understanding and analysis items discriminated better than recall items (ORs = 1.4 and 3.56, respectively). A post hoc grouping of moderate difficulty questions showed that SA + higher-order items (0.53) exceeded MCQ + recall items (0.35), with 89% vs 53% of these items, respectively, meeting a discrimination threshold of ≥0.35. In simultaneous models, question format was the strongest independent predictor of item discrimination, moderate difficulty optimized discrimination, and higher-order objectives exceeded recall. These results provide exploratory insights from a single-institution case study and suggest that adding some higher-order short-answer questions of moderate difficulty may represent a pragmatic strategy for improving assessment quality.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".