Bibliographic record
Abstract
York University introduced the elective course "Disruptive Technology Innovation and Entrepreneurship" in Winter 2024, Summer 2024, and Winter 2025, attracting students from diverse faculties, including engineering. This course utilized interdisciplinary teams for case study analysis and hackathon-style design sprints, providing engineering students a rare opportunity to collaborate across disciplines. This scholarship-of-teaching-and-learning (SoTL) paper investigates student experiences in these interdisciplinary teams, combining document analysis of 135 students' reflection journals with manual coding and generative AI to uncover key themes and dynamics. Students highlighted connections between course-based teamwork and real-world interdisciplinary scenarios like employment and extracurricular activities. Future research, building on these preliminary findings, aims to delve deeper into the challenges and benefits of interdisciplinary collaboration across faculties. This study contributes to defining the educational value and challenges of cross-faculty student teams, informing future initiatives aimed at enhancing interdisciplinary education within higher education settings.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".