Developing entrepreneurs through experiential learning: the Master of Business, Entrepreneurship and Technology program at the University of Waterloo, Canada
Bibliographic record
Abstract
Literature on entrepreneurship education identifies experience as a critical aspect of entrepreneurial development. Entrepreneurs learn by problem-solving, experimenting and making mistakes. This mode of learning is frequently at odds with traditional instruction methods in universities. Entrepreneurship courses and programs must balance entrepreneurial learning with demands for academic rigor and a tradition of classroom based instruction and assessment. Consequently, experiential learning is often an adjunct to classroom based pedagogy, or provided as an extra-curricular activity. This paper describes the development of the innovative Master of Business, Entrepreneurship and Technology program at the University of Waterloo. The core of this program is a practicum in which students develop a commercialization plan for their business or intellectual property owned by a researcher or local business. Uniquely designed courses support this experience, rather than the practicum being an adjunct of the coursework. Implications of this approach for entrepreneurship education and outcomes from the first six cohorts of students are discussed.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".