Application of best practices in university entrepreneurship education
Bibliographic record
Abstract
Purpose – The purpose of this paper is to identify and apply best practices in university entrepreneurship education to the creation of a new MBA in entrepreneurship and innovation management. It is a direct response to calls for a total re-envisioning of entrepreneurship education and criticism that existing programs lack rigour, content, pedagogy, measurement and an established definition. Design/methodology/approach – This article uses reviews of the literature to identify normative best practices and how to apply them to the new program. An entrepreneurship program design framework (EPDF) was created and applied to a new MBA program being developed in central Germany. Findings – Most studies describe aspects of current programs (e.g. lists of courses) but almost none say what should be in a program. Others provide abstract guidance (e.g. programs should define entrepreneurship) but do not give specific recommendations (e.g. what the definition should be). The proposed EPDF provided a rigorous structure for reviewing the literature, designing the new program and establishing specific best practice recommendations for defining program goals, content, pedagogy and measurement of student transformation. Research limitations/implications – The entrepreneurship literature is largely silent on normative best practice guidance, so the proposed application of best practices should be evaluated in that context. Originality/value – Previous articles present relatively abstract frameworks and concepts, whereas this article is a direct application of the practical implications of these concepts. The proposed normative best practice guidelines may be somewhat controversial, but should stimulate useful discussion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.162 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.030 | 0.018 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".