ARE MULTIDISCIPLINARY DESIGN CAPSTONE'S STUDENTS MORE INNOVATIVE THAN MONODISCIPLINARY ONES?
Bibliographic record
Abstract
Educating Innovative minds is one of the main objectives of educational institutions. In curricula, capstone design courses provide the biggest opportunity for students to be innovative and creative. To prepare students for the multidisciplinary workplace, many institutions have initiated multidisciplinary capstones besides their departmental capstones. This paper explores innovation in multidisciplinary and mechanical engineering capstone design courses. Comparing multidisciplinary and monodisciplinary capstones with regard to the students’ innovation will inform educational institutions about the best practices to prepare an environment for innovation to flourish. In this study, we define innovation as the ability to come up with creative ideas and being able to implement them. Our quantitative study measures innovation from rubrics that was assessed by supervisors and clients during the course of the projects. We also assessed innovation based on the students’ self-report. So innovation was measured from both external (supervisors) and internal (students) perspectives. Our results show that functional diversity of multidisciplinary capstones affects students' ability to be innovative.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".