Pivoting Engineering Entrepreneurship Education Following the COVID-19 Pandemic
Bibliographic record
Abstract
The spread of COVID-19 has significantly disrupted the educational landscape since March 2020.Instructors at higher education institutions had to quickly transition to an online environment for remote delivery of their academic programs. Even though academic programs are relatively easy to adapt for remote delivery as compared to other industries, educators were still tasked with redesigning their courses to guarantee the quality of education delivered to their students. This challenge is particularly true with engineering entrepreneurship educators since their course structures heavily focus on developing intangibles such as an entrepreneurial mindset and team collaboration through immersion into hands-on learning experiences. To create this experiential learning environment, engineering entrepreneurship educators have, in general, relied uponface-to-face interactions with students. Little has been published in the existing literature to report the challenges, strategies, and innovations that can help transition effectively and deliver such academic programs remotely. In this paper, the authors from four major Canadian higher education institutions report our experience from ‘trial-byfire’ mode to redesign and deliver various courses for remote learning. This paper is by no means presenting validated “best practices” but aims to trigger discussions surrounding tools available to educators considering such a transition. We hope that this paper will provide insights and strategies for our colleagues to employ in their future course design and delivery. We also hope to invite a conversation to learn more about our colleagues’ experiences and explore opportunities to identify and validate approaches for effectively teaching engineering entrepreneurship in a remote learning environment.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".