The role of co-operative education in ensuring students' success when transitioning from classroom to industry
Bibliographic record
Abstract
Engineering schools in North America aim to prepare students for transitioning to the workplace by embedding the teaching of graduate attributes within the curriculum and using robust ways to measure learning outcomes. Some of the graduate attributes for engineering students include individual and team work, communication skills, professionalism, the impact of engineering on society and the environment, ethics and equity. Our paper discusses the collaboration between technical communication instructors and the staff of the Engineering Co-op program of the Faculty of Applied Science in the University of British Columbia, Canada. Our purpose is to examine the relationship between employability and graduate attributes and to explore the experiential aspects of transformative learning in the cooperative education model. In addition to learning about graduate attributes in the classroom, students can practice them and understand them better through their co-op work experience. Three instruments for measuring the learning outcomes are discussed: 1) a reality-based assignment designed to measure students' understanding of graduate attributes; 2) a self-administered online survey; 3) a de-briefing questionnaire administered at the end of the co-op work term. The authors describe the instrument development and discuss the significance of students' progressive understanding of graduate attributes.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".