EXPLORING ENTREPRENEURSHIP EDUCATION IN ENGINEERING: INSIGHTS FROM LITERATURE AND PRACTICE REVIEWS
Bibliographic record
Abstract
Entrepreneurship education (EE), traditionally a business school endeavor, has increasingly been incorporated into educational practices in engineering and other disciplines. In this paper, we draw upon the recent literature on EE to demonstrate a shift in EE from a business-focused approach to an approach that focuses more on the growth of individuals. We also share our observations from an investigation of the website information of major entrepreneurship-focused academic programs in engineering, business and other disciplinary contexts in Canadian postsecondary institutions. The practice review shows that the curriculum for engineering entrepreneurship seems to focus more on the knowledge required for creating new ventures while skill development is also a desired learning outcome in some programs. However, a significant gap is identified in programming for fostering the entrepreneurial mindset that is needed for skill development and innovation. Our reviews also suggest the transdisciplinary characteristic of entrepreneurship education and the importance of transdisciplinary competencies to entrepreneurship education. Limitations in our methods of literature and practice reviews are discussed to inform future similar endeavors on EE.
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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 | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".