Exploring possibilities for student-staff partnerships and beyond in discipline-based education research
Bibliographic record
Abstract
Discipline-based education research (DBER) uses researchers’ disciplinary background to inform investigations into university teaching and learning. The terms of student involvement in DBER are often dictated by the researchers, with student choice limited to whether or not they will contribute data. This is counter to the ethos of active student participation in which students can directly influence their university studies. While examples of DBER projects with students as collaborators rather than as informants or subjects exist, such opportunities are usually available to a few students. This paper explores where, why, and how students could contribute to DBER projects by exploring different roles students can take and examining the possibilities for student input to the process of educational research when viewed as an investigative cycle. This identifies places where students can influence research work while being able to disengage as necessary. Going beyond individual students, whole-class contributions also appear practical, opening up the possibility to “co-create DBER.”
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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 | 0.098 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.031 |
| Scholarly communication | 0.042 | 0.031 |
| Open science | 0.004 | 0.053 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".