TEACHING ENGINEERING DESIGN AND COMMUNICATION IN FIRST YEAR USING RUBE GOLDBERG PROJECTS
Bibliographic record
Abstract
First year engineering classes tend to be very large and impersonal, which can make it difficult for instructors to engage the students. Since the first year of courses is critical in setting students up for success in engineering, being able to inspire them and give them some hands on experience during their introduction to engineering design plays a significant role in bolstering their confidence and interest as they enter more demanding and technical upper-level courses. With an aim toward achieving this inspiration and engagement, the first year engineering design and communication class at the University of Regina included the production of Rube Goldberg machines as the term project. This proved to be a very effective mechanism for teaching students how to work on a design project from start to finish. The students had fun and stretched their imaginations. As a result, the overall feedback from students was very positive, but areas for improvement have been identified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".