Beyond right or wrong: More effective feedback for formative multiple-choice tests
Bibliographic record
Abstract
INTRODUCTION: The role of feedback in test-enhanced learning is an understudied area that has the potential to improve student learning. This study investigates the influence of different forms of post-test feedback on retention and transfer of biomedical knowledge within a test-enhanced learning framework. METHODS: 64 participants from a Canadian and an Australian medical school sat two single-best-answer formative multiple choice tests one week apart. We compared the effects of conceptually focused, response-oriented, and simple right/wrong feedback on a learner's ability to correctly answer new (transfer) questions. On the first test occasion, participants received parent items with feedback, and then attempted items closely related (near transfer) to and more distant (far transfer) from parent items. In a repeat test at 1 week, participants were given different near and far transfer versions of parent items. Feedback type, and near and far transfer items were randomized within and across participants. RESULTS: Analysis demonstrated that response-oriented and conceptually focused feedback were superior to traditional right/wrong feedback for both types of transfer tasks and in both immediate and final retention test performance. However, there was no statistically significant difference between response-orientated and conceptually focused groups on near or far transfer problems, nor any differences in performance between our initial test occasion and the retention test 1 week later. As with most studies of transfer, participants' far transfer scores were lower than for near transfer. DISCUSSION: Right/wrong feedback appears to have limited potential to augment test-enhanced learning. Our work suggests that item-level feedback and feedback that identifies and elaborates on key conceptual knowledge are two important areas for future research on learning, retention and transfer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".