Turning the Tables: Involving Undergrads as Researchers in SoTL
Bibliographic record
Abstract
We report on the experience of working on a research project where students and faculty worked together as peers. The project investigated the challenges and enablers that helped or hindered faculty engage in SoTL work, and what might help encourage their colleagues to engage. The findings were instrumental in identifying components for a guide for SoTL. The findings from the study have been published elsewhere. In this paper we report on the experience of two undergraduate students who took a central role guided by experienced researchers, in collating, coding and analyzing the results, and of two experienced researchers. We share a brief overview of the project and its outcomes, provide detail of the involvement of the students and hear from them and the researchers about the experience of taking part in the project. The findings from both the original study and the student experiences will be of interest to others interested in work in this field.
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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 | 0.076 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.025 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.007 | 0.033 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".