Constructing a DC Brushless Motor- an Experiential Engineering Clinic Activity for Mechanical Students
Bibliographic record
Abstract
The Engineering Ideas Clinic initiative at the University of Waterloo focuses on designing, developing and implementing learning activities provide students with authentic, applied and hands-on experiences which integrate their learning, and expose them to genuine applications in support of their engineering science content.This paper focuses on “Constructing a Brushless DC Motor” activity which was piloted for second year Mechanical students in their “Electromechanical Devices & Power Processing” course. The main motivation behind designing this activity is the fact that the principles of electromagnetism are often taught in an abstract way, and students can struggle to link the concepts with real life applications. Hence the authors have developed this activity to materialize these abstract concepts to the students.Students in teams of three constructed a brushless DC motor, verify its construction and test its operation. In this activity students applied the principles in an experiential fun activity, compared the performance of their motors and worked collaboratively in teams.In this paper, the authors will share their experience in developing and running this activity. They will also share their observations on the students’ reaction and learning experience, as well as some comments from colleagues
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".