Team Based Learning
Bibliographic record
Abstract
Learning in teams offers unique benefits to understand and address contemporary, global, and local challenges through effective and thoughtful learning journeys. However, learning in teams is not always thoroughly planned or effectively delivered. In trying to better understand what processes support or hinder effective and innovative learning in teams, a group of researchers and practitioners explored what works and what needs to be improved in the context of one Canadian university. This article highlights the key findings from this study and offers readers strategies to support effective, innovative, and collaborative learning in teams. L’apprentissage en équipe, effectué au moyen de parcours efficaces et bien pensés, est tout particulièrement utile pour trouver des solutions aux problèmes actuels à l’échelle mondiale et locale. Toutefois, ce type d’apprentissage présente parfois des lacunes en matière de préparation et d’exécution. Dans le contexte d’une université canadienne, une équipe de chercheurs et de praticiens ont œuvré à faire la part entre ce qui fonctionne et ce qui ne fonctionne pas, de manière à savoir quels processus sont efficaces – ou non – pour obtenir un apprentissage en équipe efficace et novateur. Dans notre article, nous présentons donc les principaux résultats de cette étude et nous proposons des stratégies pour un apprentissage en équipe efficace, novateur et collaboratif.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.087 | 0.032 |
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".