Team Level Factors Affecting Innovation in Multidisciplinary Capstone Design Course
Bibliographic record
Abstract
Multidisciplinary capstones form student teams from different engineering disciplines to design, build, and test proof of concepts for an industry based project. To provide insight on multidisciplinary capstone’s performance and innovative outcomes, we explored innovation and factors related to innovation in both multidisciplinary and monodisciplinary capstones at the University of Toronto. Our investigation includes self-reported data and data from external assessments. We conducted both quantitative and qualitative research by collecting data from surveys, interviews, and video-recordings. External examiner’s and self-reported data show that multidisciplinary students are more innovative than mono-disciplinary ones. Our results show correlation between innovation and psychological safety, collaborative learning, internal and external communication, support for innovation from all parties, vision and feedback. Our research shows that aside from team’s diversity, support for innovation and culture of innovation is essential to realization of student’s creativity potential.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".