The BioExperience Research and Entrepreneurship Challenge: An iGEM-inspired applied research program for BIOSTEM talent and skills development
Bibliographic record
Abstract
Post-secondary education is falling behind in delivering the talent and skills development needed to support the growth of biology-based economies and the demands of professional and research-based graduate degree programs. Here, we describe an applied research program, the BioExperience Research and Entrepreneurship Challenge, launched in May 2020 to mitigate the impact of the COVID-19 pandemic on undergraduate experiential learning programs at the University of Ottawa, Ontario, Canada. The program provided undergraduates with meaningful talent and skills development opportunities by implementing a student-centred, project-based learning approach inspired by the International Genetically Engineered Machine (iGEM) competition. We present evidence from participant exit surveys suggesting that the program delivers a high-quality learning environment and improves learning outcomes compared to traditional work-integrated learning. Notably, 84% of respondents reported an excellent or exceptional learning experience and significant or profound improvements in skills, such as leadership (72% of respondents), problem-solving (42% of respondents) and research preparedness (52%) that are difficult to develop in conventional academic programs. Remarkably, 60% of respondents report that the job-readiness training provided by the program is better or much better than traditional work-integrated learning. Our study demonstrates that a cost-effective and scalable alternative to the iGEM competition can improve talent and skills development in BIOSTEM fields.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".