THE ART OF THE POSSIBLE: DEVELOPING A COURSE TO INTRODUCE ENGINEERING TO NON-ENGINEERS
Bibliographic record
Abstract
Understanding engineering is an important factor in fully participating in civic decision-making, however there are few opportunities for those outside engineering to learn about it. We developed and offered a course on engineering for Arts (and Commerce) students, to increase technical literacy, which counted toward the Arts degree Science requirement. A teaching team from three disciplines presented four technical modules themed around specific technologies, and covering a wide range of engineering practice topics. Students participated in many hands-on activities and demonstrations, and instructors used flipped classroom techniques. Assessment for each module consisted of both a short technical online quiz and a blog post about a topic in the news, which allowed students to bring in their own disciplinary knowledge. The final assessment was a group video project where students aimed to advocate for a position on a technical/civic issue related to one of the modules. We detail in this paper the results of our consultations with Arts; the course structure and goals; some of the specific content and activities designed (with an emphasison correctly targeting pre-existing knowledge of the students); a reflection on the successes and challenges in the first offering, including student feedback; and suggestions for others who might want to develop such a course.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".