Integrative Activities for First Year Engineering Students-Fuel Cell Cars as a Linking Project Between Chemistry, Mechatronics Concepts and Programing
Bibliographic record
Abstract
In Mechatronics Engineering at the University of Waterloo, a joint project involving small, inexpensive fuel cells cars was introduced to show how courses in the first term relate to one another. Additionally, the project was designed to provide the students with hands on learning, to give the students a taste of what to expect in later years, and to start incorporating many of the CEAB's graduate attributes at an introductory level. The fuel cell car consists of two low-voltage cells, a low power microcontroller and several sensors mounted on a motorised platform. Students employed concepts from chemistry, programming and mechatronics systems throughout the project, submitting reports at key milestones. during the projet, students needed to make decision in a team environment on which strageties to implement to meet the goals of the project. The project culminated in a final competition and report. Students were surveyed at the start, and end, and the term to measure any changes in attitude with regards to the courses as well as their satisfaction with the project. The project was well recieved by students but significant challenges remain to be solved.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".