Peer Generation of Multiple-Choice Questions: Student Engagement and Experiences
Bibliographic record
Abstract
A free online system for generation of multiple-choice questions (PeerWise) was implemented in three courses (course A, B, and C) in two different years (second and third year) of a veterinary degree program. Students were asked to author questions, and answer and rate each other's questions. Student experiences of the system were explored using an online survey. The majority of students in both years either agreed or strongly agreed that both authoring and answering questions was helpful for their studies and wanted to use the system again in future courses. Thematic analysis highlighted students' views that engaging with the resource increased breadth and depth of knowledge and understanding and was very useful for revision purposes. There was a statistically significant difference between students in second and third year regarding whether students felt it was necessary for academic staff to be involved in the review process. Thematic analysis of this aspect identified issues relating to confidence in the ability of the peer group and the need for reassurance in the second-year group. Student engagement with the system was correlated with examination performance. In courses A and B there was a positive correlation between number of questions answered and examination performance, in course C there was no correlation. This study highlights the benefits of peer activity around question generation and proposes that such activities are an efficient and effective means to support student learning.
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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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".