Engineering Outreach Using a Hands-On Case Study Approach
Bibliographic record
Abstract
University-led outreach programs have been active in Canada for over 20 years. Today, more than 200,000 students participate in these programs with a variety of activities such as camps, workshops, and community outreach programs [1]. Almost three quarters of these students participated in workshops, including in-class led by teachers with materials provided by Universities as well as on-campus activities. At the University of Waterloo, the Engineering Science Quest (ESQ) program has been running for 20 years to expose elementary school children to hands-on science and engineering. The goals of the program are to excite children about science, technology, engineering and math (STEM), and to show them through hands-on activities that these can be directly applied to solve real-world problems. A secondary goal is to improve their scientific, engineering and technological literacy [2]. All activities are designed to complement the Ontario elementary school curriculum. About 2200 students participate in ESQ, in over 100 one-week sessions, each summer. ESQ runs a further 200 workshops in elementary classrooms.Until recently, Waterloo did not run workshops designed for a high school audience. The focus to date of ESQ activities has been on elementary school audiences, to promote continued interest in science and engineering. For the older high school audience, a need was identified to more strongly reflect the work done in a post-secondary environment. This has been done through participation in First Robotics, and recent „Designing the Future‟ events, wherein students are exposed to engineering design through a combination of hands-on design exercises, lectures, and displays of University student projects. To further develop these workshops, the Outreach group has joined forces with the Waterloo Cases in Design Engineering (WCDE) group to integrate more realistic contexts and computer simulations into these activities.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".