Score Increase and Partial-Credit Validity When Administering Multiple-Choice Tests Using an Answer-Until-Correct Format
Bibliographic record
Abstract
There are numerous benefits to answer-until-correct (AUC) approaches to multiple-choice testing, not the least of which is the straightforward allotment of partial credit. However, the benefits of granting partial credit can be tempered by the inevitable increase in test scores and by fears that such increases are further contaminated by a large random guessing component. We have measured the effects of using the immediate feedback assessment technique (IF-AT), a commercially available AUC response system, on the scores of a typical first-year chemistry multiple-choice test. We find that with a particular commonly used scoring scheme the test scores from IF-AT deployment are 6–7 percentage points higher than from Scantron deployment. This amount is less than that suggested by previous studies, where the mark increase was calculated in a purely post hoc manner and thus neglected affective changes of students’ behavior associated with the IF-AT technique. Furthermore, we have strong evidence that partial credit is awarded in a highly rational manner in accordance with the students’ level of understanding.
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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 | 0.022 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".