The Creation of a Collaborative, Case-Based Learning Experience in a Large-Enrollment Classroom
Bibliographic record
Abstract
Numerous educational studies have shown that passive learning methods are frequently associated with disappointing learning outcomes, yet many faculty instructors continue to rely on passive didactic lectures. This article describes the creation of an active learning teaching approach-referred to as the collaborative, case-based classroom-that combines three pedagogical strategies: peer-assisted learning, case-based learning, and just-in-time teaching. Data from student surveys of a third-year cardiology elective showed a preference for this teaching approach compared with a case-based lecture. Six major themes emerged from survey analysis: engagement/interactivity, instructional benefit, clinical reasoning, clinical relevance, peer-assisted learning, and timely feedback. Although detailed here in the context of a cardiology elective, the collaborative, case-based classroom is a teaching approach that could be modified to fit a variety of other teaching environments.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".