Case Based Learning Teaching Methodology in Undergraduate Health Sciences
Bibliographic record
Abstract
Case-based learning (CBL) is an interactive teaching approach involving small-group discussion to determine a range of solutions for a presented patient case. In light of the success that the approach has achieved in numerous professional and undergraduate programs, a pilot project was introduced in 2009 by senior health sciences students, who acted as CBL facilitators, at the University of Ottawa for undergraduate courses in the Interdisciplinary School of Health Sciences (ISHS). In collaboration with faculty professors, the facilitators developed CBL sessions consisting of patient cases that were reflective of the core objectives of health sciences courses. A total of 144 undergraduate students from three ISHS courses took part in these sessions; they were evaluated based on the calibre of their participation and a quiz. The quiz consisted of 5 questions that evaluated the students’ mastery of the concepts covered in the CBL session. The students also completed an evaluation of the pilot project. On a nominal scale of one to five, the students on average scored 4.13 out of a possible 5.00 (SD 1.48) marks on the quiz. In the evaluation, the students rated the project as having an overall learning benefit of 3.82 on a nominal scale of one to four. The evaluation indicates that the students perceived the program as having significant learning value and the quiz marks confirmed that CBL promoted the application of lecture content to practical scenarios. These preliminary findings suggest that implementing CBL in ISHS would enhance students’ academic experience. Further sessions based on this model would improve from more rigorous pre- and post- session assessments.
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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 | 0.039 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".