Student feedback in problem based learning: a survey of 103 final year students across five Ontario medical schools
Bibliographic record
Abstract
CONTEXT: Problem based learning (PBL) has become an integral component of medical curricula around the world. In Ontario, Canada, PBL has been implemented in all five Ontario medical schools for several years. Although proper and timely feedback is an essential component of medical education, the types of feedback that students receive in PBL have not been systematically investigated. OBJECTIVES: In the first multischool study of PBL in Canada, we sought to determine the types of feedback (grades, written comments, group feedback from tutor, individual feedback from tutor, peer feedback, self-assessment, no feedback) that students receive as well as their satisfaction with these different feedback modalities. SUBJECTS AND METHODS: We surveyed a sample of 103 final year medical students at the five Ontario schools (University of Toronto, McMaster University, Queens University, University of Ottawa and University of Western Ontario). Subjects were recruited via E-mail and were asked to fill out a questionnaire. RESULTS: Many students felt that the most helpful type of feedback in PBL was individual feedback from the tutor, and indeed, individual feedback was one of the more common types of feedback provided. However, although students also indicated a strong preference for peer and group feedback, these forms of feedback were not widely reported. There were significant differences between schools in the use of grades, written comments, self-assessment and peer feedback, as well as the immediacy of the feedback given. CONCLUSIONS: Across Ontario, students do receive frequent feedback in PBL. However, significant differences exist in the types of feedback students receive, as well as the timing. Although rated highly by students at all schools, the use of peer feedback and self-assessment is limited at most, but not all, medical schools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 teacher head, 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".