Bringing Teamwork and Experiential Learning to Large First Year Classes: The UTSC Approach and the Student Perspective
Bibliographic record
Abstract
Experiential learning and having students work in teams are two educational contexts that allow students to exercise the sorts of skills that bring success in post-graduation life: skills like critical and creative thought, effective communication and collaboration, and the ability to not only see one’s strengths and weakness, but also the skills needed to grow personally. Typically, these sorts of experiences are only offered in smaller upper-year courses simply because of the logistics involved in managing such work. This paper argues that the development of such skills is too important to wait until third or fourth year, especially given the accelerated advance in AI technologies which make these human skills more important than ever to student success. In this report, the University of Toronto Scarborough Approach to Teamwork and Experiential Learning is described, which works even with extremely large first-year courses. Research data is given, describing an implementation of this approach within an 1800-student introductory course and showing that not only can such an approach be used without causing student dissatisfaction, but rather, students find it interesting, see its value, and feel competent throughout.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".