Students as partners in learning and teaching: The benefits of co-creation of the curriculum
Bibliographic record
Abstract
This research explores the benefits of co-creation of the curriculum, which is seen as one form of student-staff partnership in learning and teaching in which each partner has a voice and a stake in curriculum development. This qualitative research analyses participants’ perceptions of co-creation of the curriculum in the Scottish higher-education sector. Initial findings show that some staff and students participating in co-creation of the curriculum perceive it to benefit them by (a) fostering the development of shared responsibility, respect, and trust; (b) creating the conditions for partners to learn from each other within a collaborative learning community; and (c) enhancing individuals’ satisfaction and personal development within higher education. Using Barnett’s conceptualisation of supercomplexity and Baxter Magolda’s three-pronged view of self-authorship, the author suggests that critical and democratic engagement in co-creation of the curriculum can develop the self-authorship of both students and staff members, including their cognitive, interpersonal, and intrapersonal abilities which help them adapt to an ever-changing, supercomplex world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".