Reliability of multiple-choice versus problem-solving student exam scores in higher education: Empirical tests
Bibliographic record
Abstract
Instructors in higher education frequently employ examinations composed of problem-solving questions to assess student knowledge and learning. But are student scores on these tests reliable? Surprisingly few have researched this question empirically, arguably because of perceived limitations in traditional research methods. Furthermore, many believe multiple choice exams to be a more objective, reliable form of testing students than any other type. We question this wide-spread belief. In a series of empirical studies in 8 classes (401 students) in a finance course, we used a methodology based on three key elements to examine these questions: A true experimental design, more appropriate estimation of exam score reliability, and reliability confidence intervals. Internal consistency reliabilities of problem-solving test scores were consistently high (all > .87, median = .90) across different classes, students, examiners, and exams. In contrast, multiple-choice test scores were less reliable (all < .69). Recommendations are presented for improving the construction of exams in higher education.
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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 | 0.104 | 0.343 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".