Virtual Learning Environment for Entrepreneurship: A Conceptual Model
Bibliographic record
Abstract
The University of Waterloo has a history as an innovative and entrepreneurial university. With increasing demand for entrepreneurship education and venture development support there has been increasing interest in how to provide this support virtually. To address this need, an entrepreneurship platform consisting of four primary components; entrepreneurial team engagement, mentor engagement, provision of 'just-in-time' learning resources, and social network creation is under development. Engagement and social network creation are built around a series of gamified events that provide structure and feedback for the participants, as well as focal points for mentoring and network development. The 'embedding' of these early-stage ventures into a supportive social network aligns with a belief that one does not simply launch new ventures, but rather launch networks. These event gates are supported by a system of 'just-in-time' learning modules allow the participants to develop their own learning program, and may be drawn upon as needed. In this paper we discuss the conceptual model as well as progress on development of its key features. We also discuss some of the early results and lessons learned integrating it into several initiatives underway in Canada and Kenya.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".