If we assess, will they learn? Students’ perspectives on the complexities of assessment-for-learning
Bibliographic record
Abstract
Introduction: Assessment can positively influence learning, however designing effective assessment-for-learning interventions has proved challenging. We implemented a mandatory assessment-for-learning system comprising a workplace-based assessment of non-medical expert competencies and a progress test in undergraduate medical education and evaluated its impact. Methods: We conducted semi-structured interviews with year-3 and 4 medical students at McGill University to explore how the assessment system had influenced their learning in year 3. We conducted theory-informed thematic analysis of the data. Results: Eleven students participated, revealing that the assessment influenced learning through several mechanisms. Some required little student engagement (i.e., feed-up, test-enhanced learning, looking things up after an exam). Others required substantial engagement (e.g., studying for tests, selecting raters for quality feedback, using feedback). Student engagement was moderated by the perceived credibility of the system and of the costs and benefits of engagement. Credibility was shaped by students' goals-in-context: becoming a good doctor, contributing to the healthcare team, succeeding in assessments. Discussion: Our assessment system failed to engage students enough to leverage its full potential. We discuss the inherent flaws and external factors that hindered student engagement. Assessment designers should leverage easy-to-control mechanisms to support assessment-for-learning and anticipate significant collaborative work to modify learning cultures.
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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 | 0.032 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".