Examination of the Quality of Multiple-choice Items on Classroom Tests
Bibliographic record
Abstract
Because multiple-choice testing is so widespread in higher education, we assessed the quality of items used on classroom tests by carrying out a statistical item analysis. We examined undergraduates’ responses to 1198 multiple-choice items on sixteen classroom tests in various disciplines. The mean item discrimination coefficient was +0.25, with more than 30% of items having unsatisfactory coefficients less than +0.20. Of the 3819 distractors, 45% were flawed either because less than 5% of examinees selected them or because their selection was positively rather than negatively correlated with test scores. In three tests, more than 40% of the items had an unsatisfactory discrimination coefficient, and in six tests, more than half of the distractors were flawed. Discriminatory power suffered dramatically when the selection of one or more distractors was positively correlated with test scores, but it was only minimally affected by the presence of distractors that were selected by less than 5% of examinees. Our findings indicate that there is considerable room for improvement in the quality of many multiple-choice tests. We suggest that instructors consider improving the quality of their multiple-choice tests by conducting an item analysis and by modifying distractors that impair the discriminatory power of items. Étant donné que les examens à choix multiple sont tellement généralisés dans l’enseignement supérieur, nous avons effectué une analyse statistique des items utilisés dans les examens en classe afin d’en évaluer la qualité. Nous avons analysé les réponses des étudiants de premier cycle à 1198 questions à choix multiples dans 16 examens effectués en classe dans diverses disciplines. Le coefficient moyen de discrimination de l’item était +0.25. Plus de 30 % des items avaient des coefficients insatisfaisants inférieurs à + 0.20. Sur les 3819 distracteurs, 45 % étaient imparfaits parce que moins de 5 % des étudiants les ont choisis ou à cause d’une corrélation négative plutôt que positive avec les résultats des examens. Dans trois examens, le coefficient de discrimination de plus de 40 % des items était insatisfaisant et dans six examens, plus de la moitié des distracteurs était imparfaits. Le pouvoir de discrimination était considérablement affecté en cas de corrélation positive entre un distracteur ou plus et les résultatsde l’examen, mais la présence de distracteurs choisis par moins de 5 % des étudiants avait une influence minime sur ce pouvoir. Nos résultats indiquent que les examens à choix multiple peuvent être considérablement améliorés. Nous suggérons que les enseignants procèdent à une analyse des items et modifient les distracteurs qui compromettent le pouvoir de discrimination des items.
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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 | 0.023 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".