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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".