Engaging Engineering Students with Engineering Entrepreneurship and the Start-Up Working Environment through Supervised Entrepreneurial Work-Integrated Learning
Bibliographic record
Abstract
Higher-education institutions are seeing an increasing interest in entrepreneurship education across the disciplines, engineering programs included. With a parallel growing emphasis on work-integrated learning opportunities for students, a unique opportunity is presented with an Entrepreneurial Work-Integrated Learning (EWIL) pedagogy, where entrepreneurship education is delivered through the application of work-integrated learning pedagogy. Supervised Entrepreneurial Work-Integrated Learning (sEWIL) is a particular modality of EWIL, where engineering students learn about entrepreneurship through participation in a start-up working environment, where students directly observe and participate in the entrepreneurial working environment. sEWIL offers students an authentic real-world learning environment where tacit entrepreneurial knowledge is acquired, knowledge that cannot be taught through in-class traditional teaching practices. Through purposeful reflection, engineering students are confronted with the question of their professional and personal identities and their compatibility to the start-up working environment, whether as entrepreneurs or as working engineering professionals. The sEWIL pedagogy is presented and discussed through a work-integrated learning quality framework.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".