WHAT CONSTITUTES A MULTIDISCIPLINARY CAPSTONE DESIGN COURSE? BEST PRACTICES, SUCCESSES AND CHALLENGES.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it