Major Design Capstone Projects: A Multidisciplinary Teaching and Learning Approach
Bibliographic record
Abstract
Before 2020, Major Design Capstone Project (MDCP) courses in Mechanical Engineering and in Electrical Engineering and Computer Engineering departments involved teams of students engaged in capstone projects, which often required expertise beyond the scope of their disciplinary training. With the new robotic program, the purpose of the new multidisciplinary MDCP courses is to develop a unified framework that enables mechanical, electrical, computer and robotic engineering students to effectively work together by combining the disciplinary expertise required to achieve their projects. The multidisciplinary MDCP framework involves an open call for project ideas, multidisciplinary assessments by groups of students, three courses over the last three semesters of their undergraduate curricula, multidisciplinary team teaching, and skill-based evaluations. The resulting capstone projects improve in quality, efficiency, and complexity. Having the opportunity to combine their expertise with other engineering fields creates a real teamwork learning experience that enhances students preparation for their future professional practice.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".