Engineering Co-op and Internship Experiences: The Roles of Workplaces, Academic Institutions and Students
Bibliographic record
Abstract
Work-integrated learning, particularly in theform of co-ops and internships, has long been an integralpart of many engineering programs. While recentgovernment interest in work-integrated learning hasraised its profile, it is unclear how the three main actors –the workplace, the academic institution and studentsthemselves – interact with each other to enhance students’learning experiences and outcomes. This paper attemptsto fill this gap by examining engineering co-op andinternship literature as well as programming practices atnineteen North American universities. In light of aconceptual framework foregrounding organizationalstructure, human agency and learning outcomes, weidentified five themes that demonstrated the interactionsbetween organizational and individual factors involved inthe workplace learning process of engineering co-ops andinternships. The paper contributes to the discussion onwork-integrated engineering education by highlightingthe usefulness of the conceptual framework to empiricalresearch on workplace learning and the practicalimplications of the findings for engineering educators,employers, and engineering co-op and internshipstudents.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".