A Baseline for Multiple-Choice Testing in the University Classroom
Bibliographic record
Abstract
There is a broad literature in multiple-choice test development, both in terms of item-writing guidelines, and psychometric functionality as a measurement tool. However, most of the published literature concerns multiple-choice testing in the context of expert-designed high-stakes standardized assessments, with little attention being paid to the use of the technique within non-expert instructor-created classroom examinations. In this work, we present a quantitative analysis of a large corpus of multiple-choice tests deployed in the classrooms of a primarily undergraduate university in Canada. Our report aims to establish three related things. First, reporting on the functional and psychometric operation of 182 multiple-choice tests deployed in a variety of courses at all undergraduate levels of education establishes a much-needed baseline for actual as-deployed classroom tests. Second, we motivate and present modified statistical measures—such as item-excluded correlation measures of discrimination and length-normalized measures of reliability—that should serve as useful parameters for future comparisons of classroom test psychometrics. Finally, we use the broad empirical data from our survey of tests to update widely used item-quality guidelines.
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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 | 0.059 | 0.242 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".