User experience of the Written Exam Question Quality tool to inform the writing of new written-exam questions
Bibliographic record
Abstract
Background: Creating new written-exam questions is a burdensome task for faculty members. While several guidelines exist, there had not been a previous attempt to streamline them in a user-friendly tool. We created the Written Exam Question Quality tool (WEQQ) and explored potential users' perception of this tool when writing their exam questions. Methods: We conducted a descriptive study to explore how four Canadian faculty members used the WEQQ. We conducted structured interviews that were analyzed within and across participants to understand the latter's perceived usefulness and acceptability of the WEQQ. Quantitative data from a short questionnaire on creating exam questions and their psychometric properties were also collected. Results and conclusion: Participants' perception of the WEQQ was positive, and they were favorable to its use. The WEQQ seemed to represent a user-friendly, easy way to help faculty members in creating multiple-choice or short-answer questions. Time on task remained the same when using the WEQQ. We were able to identify two user profiles, passive and active, which indicated how faculty members use the WEQQ to create exam questions. Future steps would be to further investigate if the WEQQ can increase the quality of written-exam questions and to understand how to promote an active use of the WEQQ when implementing this tool.
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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 | 0.017 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".