Co-researching co-creation of the curriculum: Reflections on arts-based methods in education and connections to healthcare co-production
Bibliographic record
Abstract
Learning through experience is an important, creative, and fulfilling way to apply theory to practice. In this essay, we explore our experiences of co-researching how students and staff conceptualise co-creation of the curriculum. We each have multi-faceted roles in higher education as we study, work, and contribute to formal student representation processes. At the time of this project, I (Tanya) was working at the Edinburgh University Students’ Association, supporting student representation, and I (Hermina) was a first-year student representative from the School of Health in Social Science. It was through a University of Edinburgh Innovative Initiative Grant project related to Tanya’s PhD research (focusing on co-creation of the curriculum) that we began to work together closely. We are both passionate about becoming involved in collaborative initiatives that improve the student experience and the wider university community. We were interested in exploring how our individual experiences as co-researchers could bridge boundaries between the traditional roles of postgraduate and undergraduate students, staff and students, and researchers and participants. Our aim was to blur the lines between these roles by working collaboratively with students-as-partners, facilitating open dialogue about best practices in learning and teaching, and redistributing power to create new synergies. Below, we focus on these topics and the little-explored connections between our academic disciplines in which co-creation of higher education curricula and co-production of health care are each beginning to play important roles. We reflect on our experiences of engaging in collaborative research using deliberative-democratic and arts-based methods, and we aim to provide an informative account of our experiences while drawing new connections.
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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 | 0.078 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.096 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".