Curriculum enhancement through co-creation: Fostering student-educator partnerships in higher education
Bibliographic record
Abstract
This case study presents an institutional approach to curriculum enhancement and co-creation. It explores how these two elements of a university’s strategy interlink through institutional values, curriculum development initiatives, and the advent of a new recognition scheme for student co-creators at Queen Mary University of London in the UK. It explores how the delivery of curriculum enhancement projects has been made possible through co-creation with students and discusses its outcomes: curriculum enhancement resources for staff and students, recognition for students, and joint presentations and publications. This case study also reflects on the experience of student co-creators and the benefits and challenges for staff and the institution, considers the specific contexts required to promote a shift in institutional culture towards co-creation, and shares successes and recommendations for implementing this approach.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".