Curricular Hackathons for Engineering Design Learning: The Case of Engineering Design Days
Bibliographic record
Abstract
Contribution – This paper defines “curricular hackathons” and describes the features, challenges, and opportunities of the format based on experience implementing a series of curricular hackathons which provide students with formative design experiences where they solve ill-structured and integrative design problems. These curricular hackathons provide students with an opportunity to develop their design skills, professional skills, and design self-efficacy. Background – Design is an essential part of engineering practice. However, undergraduate engineering students are provided few opportunities to develop the skills necessary for solving ill-structured and complex design problems prior to their final year capstone projects. Cornerstone projects and other summative activities are common attempts to introduce students to design, but often carry significant academic weight in the host course. Adapting extra-curricular hackathons to the curriculum could provide a useful pedagogy for formative design practice. Intended Outcomes – The University of Waterloo has adapted the model of a hackathon (short duration, intensive, and social, design experiences) to give students additional hands-on experience with engineering practice. The short duration makes these activities easier to schedule than other curricular integration approaches, and the highly social nature of these activities provides a fertile environment for development of design self-efficacy. Application Design – Engineering Design Days are 2-day long design-build-test learning activities that fit the model of a “curricular hackathon”. Findings – Synthesizing prior studies reporting on the use of curricular hackathons to each engineering design skills, and using an interview study with instructors who have designed and implemented these activities, we report on the ability of curricular hackathons to guide students through the entire design-build-test cycle and the ways in which these activities have been embedded into the curriculum.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".