Cultivating An Entrepreneurial Mindset: A Quasi-Experimental Study of Entrepreneurial Education
Bibliographic record
Abstract
Entrepreneurship education goes beyond preparing students for new venture creation by developing an entrepreneurial mindset that acts as a guiding cognitive framework essential for navigating the complexities of the modern workplace. This study examines the impact of two pedagogical approaches to entrepreneurship education (i.e., competence-based vs. supply-demand) on an entrepreneurial mindset (i.e., failing forward, tolerance for ambiguity, responsiveness to feedback, and teamwork attitude). A quasi-experimental design involving over 1,300 undergraduate students was used to assess changes in entrepreneurial mindset and entrepreneurial intentions. The findings reveal that the competence-based model, grounded in constructivist and experiential learning principles, leads to significant improvements in failing forward, responding to feedback, and teamwork attitudes, but not tolerance for ambiguity. Interestingly, entrepreneurial intentions showed a decrease over time in both courses, but students that were able to develop responsiveness to feedback, were buffered against this effect. Additionally, online course delivery formats showed advantages over traditional face-to-face methods in enhancing several aspects of the entrepreneurial mindset. Keywords: Entrepreneurship Education, Entrepreneurial Mindset, Entrepreneurial Intention, Experiential Learning, Online Learning
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".