Bibliographic record
Abstract
We describe how an answer-until-correct multiple-choice (MC) response format allows for the construction of fully multiple-choice examinations designed to operate much as a hybrid between standard MC and constructed-response (CR) testing. With this tool—the immediate feedback assessment technique (IF-AT)—students gain complete knowledge of the correct answer for each question during the examination and can use such information for solving subsequent test items. This feature allows for the creation of a new type of context-dependent item set: the “integrated testlet.” In an integrated testlet, certain items are purposefully inter-dependent and are thus presented in a particular order. Such integrated testlets represent a proxy of typical CR questions, but with a straightforward and uniform marking scheme that also allows for granting partial credit for proximal knowledge. As proof-of-principle, we present a case study of an IF-AT-scored midterm and final examination for an introductory physics course and discuss specific testlets possessing varying degrees of integration. In total, the polychotomously scored items are found to allow for excellent discrimination, with a mean item-total correlation measure for the combined 45 items of the two examinations of r¯′=0.41±0.13 (mean ± standard deviation) and a final examination test reliability of α = 0.82 (n = 25 items). Furthermore, partial credit is shown to be allocated in a discriminating and valid manner in these examinations. As has been found in other disciplines, the reaction of undergraduate physics students to the IF-AT is highly positive, further motivating its expanded use in formal classroom assessments.
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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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".