Measuring Student Transformation in Entrepreneurship Education Programs
Bibliographic record
Abstract
This article describes how to measure student transformation primarily within a university entrepreneurship degree program. Student transformation is defined as changes in knowledge (“Head”), skills (“Hand”), and attitudinal (“Heart”) learning outcomes. Following the institutional impact model, student transformation is the primary goal of education and all other program goals and aspects of quality desired by stakeholders are either input factors (professors, courses, facilities, support, etc.) or output performance (number of startups, average starting salary, % employment, etc.). This goal‐setting framework allows competing stakeholder quality expectations to be incorporated into a continuous process improvement (CPI) model when establishing program goals. How to measure these goals to implement TQM methods is shown. Measuring student transformation as the central focus of a program promotes harmony among competing stakeholders and also provides a metric on which other program decisions (e.g., class size, assignments, and pedagogical technique) may be based. Different stakeholders hold surprisingly different views on defining program quality. The proposed framework provides a useful way to bring these competing views into a CPI cycle to implement TQM requirements of accreditation. The specific entrepreneurial learning outcome goals described in the tables in this article may also be used directly by educators in nonaccredited programs and single courses/workshops or for other audiences.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".