WHAT CONSTITUTES A MULTIDISCIPLINARY CAPSTONE DESIGN COURSE? BEST PRACTICES, SUCCESSES AND CHALLENGES.
Bibliographic record
Abstract
This paper reflects on how to practicallyachieve the necessary organizational and curricularpreconditions that allow for conducting MultiInterandTrans – disciplinary engineering capstone designindustrysponsored projects and thereby serveengineering graduates better. The differences betweenoffering multidisciplinary and the common singledisciplinarycapstone design courses are also highlighted.Simultaneously, this paper focuses on the key challengesthat aggravate the smooth implementation of such acomplex undertaking the fundamental goal of which is tooffer a multidisciplinary team-based design project workexperience to the Students that would be mimicking asclose as possible an analogue typical industrial setting.Possible remedial measures for overcoming thesechallenges are also discussed. A new multidisciplinarycapstone design project course offered at the Faculty ofApplied Science and Engineering (FASE) at theUniversity of Toronto in the 2013/14 academic year(“APS 490Y Multidisciplinary Capstone Design”)coordinated by Prof. Kamran Behdinan served as thebasis for this work.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".