PROMOTING COLLABORATIVE, SELF-DIRECTED, HANDS-ON LEARNING IN ENGINEERING DESIGN IN UNDERGRADUATE AND GRADUATE TEACHING
Bibliographic record
Abstract
The face of engineering education is rapidly changing as more emphasis is placed on a self-directed, problem-based and design-driven approach. The School of Engineering at the University of Guelph has recognized the importance of engineering design education by introducing capstone design courses and encouraging incorporation of design in many senior-level courses. Two recent initiatives include inclusion of a student-led laboratory design project in the Mass Transfer Operations course (ENGG*3470) and a self-directed, problem-based approach to teaching a new graduate course in Pollution Prevention Engineering (ENGG*6790). Both courses placed a significant emphasis on “learning by doing” and importance of “self-directed learning”. Both courses also encouraged the development of various design skills such as problem definition, information collection, collaboration, innovation, communication, life-cycle costing, etc. This paper provides insights on these two courses and the approaches used to ensure a collaborative, hands-on and self-directed learning experience for students.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".