To quiz or not to quiz: Formative tests help detect students at risk of failing the clinical anatomy course
Bibliographic record
Abstract
Through a modified team-based learning (TBL) in the anatomy pre-clerkship curriculum, formative evaluations are utilized in the University of Ottawa Faculty of Medicine to assess and predict students' outcomes on summative examinations. The purpose of this study was to determine the efficiency of formative assessments to predict student's performance on summative examinations, during the first two semesters of medical school. Formative assessments included multiple-choice quizzes (MCQ) for each laboratory session and a practical midterm examination (MIDTERM), while the summative assessment corresponded to the final practical examination (FINAL). A moderate correlation between MCQs and FINAL (r = 0.353 and 0.301, respectively), and strong correlation between MIDTERM and FINAL assessments (r = 0.688 and 0.610, respectively) were found in the first two semesters. The MIDTERM-FINAL correlations were enhanced for students who scored under 61% in the MIDTERM (r = 0.887 and 0.717, respectively). Despite limitations, mostly related to particularities of the used tests, the analysis revealed an efficient method to identify students at risk of failing the FINAL in a TBL-based anatomy program. Future developments include the elaboration of strategies to predict and support those underperforming students.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".