Assessing the quality of a student-generated question repository
Bibliographic record
Abstract
We present results from a study that categorizes and assesses the quality of questions and explanations authored by students in question repositories produced as part of the summative assessment in introductory physics courses over two academic sessions.Mapping question quality onto the levels in the cognitive domain of Bloom's taxonomy, we find that students produce questions of high quality.More than threequarters of questions fall into categories beyond simple recall, in contrast to similar studies of studentauthored content in different subject domains.Similarly, the quality of student-authored explanations for questions was also high, with approximately 60% of all explanations classified as being of high or outstanding quality.Overall, 75% of questions met combined quality criteria, which we hypothesize is due in part to the in-class scaffolding activities that we provided for students ahead of requiring them to author questions.This work presents the first systematic investigation into the quality of student produced assessment material in an introductory physics context, and thus complements and extends related studies in other disciplines.
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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 | 0.056 | 0.353 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".