Undergraduates as course creators: Reflections on starting and sustaining a student-faculty partnership
Bibliographic record
Abstract
We reflect on a grassroots partnership to create a new sophomore seminar, “Precision medicine or privileged medicine?” The course puts students in the driver’s seat to explore problems with inclusiveness and quality of biomedical research. It also emphasizes the relevance of “soft skills”, such as emotional intelligence, to build trust, understand inequities and involve patients as partners to improve research. After developing the course, we felt our experience could interest students and faculty interested in pedagogical co-design, especially where this is not supported by a specific initiative. Ours was a multi-year partnership spearheaded by a sophomore, a senior and a volunteer adjunct associate professor. While we made important use of existing university programs, there was no umbrella initiative or overall sponsor to support student-faculty partnerships from first ideas through new course implementation. This meant we operated with both uncertainty and creativity regarding process, continuity and funding. In this essay we reflect on experiences that formed and facilitated our partnership, from our first conversations through course approval. We look at how our partnership became one of pedagogical co-design and shaped an inclusive, real-world problem-oriented course for undergraduates. Our reflections may help students interested in teaching and course development looking to develop relationships with faculty members in which they feel heard and valued. Furthermore, we hope to encourage faculty members looking to create a course that prioritizes student experience, goals, and feedback, through development of collaborative and mutually beneficial partnerships with undergraduates.
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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 | 0.047 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.035 | 0.023 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.007 | 0.037 |
| Research integrity | 0.010 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 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".