Theory-Based Design of an Entrepreneurship Microcredentialing and Modularization System Within a Large University Ecosystem
Bibliographic record
Abstract
The theoretical design framework and implementation of the Entrepreneurship Microcredentialing and Modularization System at Ryerson University in Toronto, Canada, is described, one of the largest programs in the world with 75 different entrepreneurship courses, 10 on-campus incubators, several masters degrees, undergraduate degrees, majors, minors, and cocurricular levels of credentialing. Over 6,500 students per year receive an entrepreneurship course credit and over 2,000 of these accumulate microcredentials to achieve this course credit. To reduce the likelihood that students merely accumulate random modules and microcredentials, it is critical to use an integrating framework so that these modules build toward a greater integrated whole (i.e., a curriculum). This article describes how a Microcredentialing and Modularization System is currently used in six courses which build toward different credentials and program learning outcome measurements for accreditation purposes. University resources and integrating mechanisms are described as well as lessons learned during implementation. The primary theoretical contribution of this article is to extend the theoretical framework used for accredited program-level design for small cohorts of entrepreneurship students into a university-level ecosystem design. The primary practical contribution of this article is a detailed case study description of one of the world’s largest and most comprehensive university entrepreneurship ecosystems.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".