Embedding design thinking, career planning and teamwork into the curriculum better prepares life science students for research and alternate science careers
Bibliographic record
Abstract
Many undergraduates have either a narrow or vague idea of their career path which causes stress and can result in them leaving science. In addition, they often lack confidence or the knowledge needed to pursue independent research project courses in their senior year. To broaden student career perspectives, promote adaptability and build research skills, we developed a third-year course: Research Readiness and Advancing Biomedical Discoveries. This flipped course features online pre-class modules and extensive in-class group work. Online module topics include: writing a research proposal, project management and attributes of a successful scientist ( https://experientialmodules.utoronto.ca/research-readiness/ ). We also incorporated design-your-life ( http://lifedesignlab.stanford.edu/ ) team activities (DYL) such as brainstorming, articulating alternate life plans (Odyssey planning), and networking to help students develop novel scientific research proposals and to support their own creative career planning. To assess student perceptions of course components, we conducted anonymous student surveys three times in each offering of the course using both 5-point Likert scales and open-ended prompts for student feedback (Univ. Toronto REB#18345). Responses to open-ended survey questions were independently analyzed for broad themes by two third-party research assistants. Over the three years of course offerings, 86% of students reported feeling better prepared for research and other opportunities after graduation, 87% of students reported being encouraged to consider flexible career paths, and 87% reported they have realized the importance of teamwork (n=121). Written comments included: “ The research proposal was an incredible assignment because it gave me insight into what I would be doing in research and let me think of my own idea.” and “ [Odyssey planning] helps me to rethink my choices. Why can’t I make different choices? What is holding me back?. I think I need more diverse experience in area of biomedical science to answer these questions. ” Thus, students perceive that our novel course better prepares students for research and alternate science careers. Learning and Education Advancement Fund (LEAF) Impact grant, University of Toronto, Career Readiness Fund, Faculty of Arts and Science, University of Toronto, Online Undergraduate Course Initiative Fund, University of Toronto This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".