BETS – Innovating on Co-op to Enhance Engineering Education
Bibliographic record
Abstract
The Business Employment Transferable Skills (BETS) program was a pilot project established for unemployed first year Waterloo Engineering students to train them in entrepreneurial skills and provide them with experience in start-up companies. Twenty students participated in the BETS program and they were “hired” in a similar competitive manner used for regular co-op jobs. The students were from 8 different engineering programs and had completed 8 months of academic study prior to entering the program. Students initially completed 80 hours of training to build workplace skills required to succeed in an entrepreneurial work place. Employers submitted a one-page form, describing a proposed project and the BETS coordinator “matched” them with teams of two students for 3 week work placements. Each student completed a total of 4 placements over a 12 week period. A total of 29 start-ups, with limited financial resources to staff projects, in local technology incubators participated. Most were in various ICT sectors however a few other sectors were represented. Most companies had fewer than 5 employees and most personnel were non-salaried “founders”. Students worked on a range of projects including web site development, market research, data gathering and database development, mobile app development and product testing. At the end of each placement the students received an assessment of their performance by the employers. The students completed an assessment of the work placement where they identified skills developed, challenges encountered and successes achieved. BETS was well received by students and employers. Companies benefited from completion of short-term projects and developed a rapport with potential future employees. BETS students gained insight into start-ups and relevant, transferable and marketable skills and outcompeted classmates in the next co-op round. The lessons learned during the trial will be presented at the conference.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".