A co-creation of learning and teaching typology: What kind of co-creation are you planning or doing?
Bibliographic record
Abstract
In higher education discourse, reference to co-creation, partnership, and student engagement (to name but a few of the commonly used terms), covers a very wide range of different research and practice. This variety can often be confusing. In response, I present a co-creation oflearning and teaching typology, which is a practical resource intended to support students and staff to reflect on, and discuss, their planned and current practice and to be able to identify what particular kind of co-creation they are planning or doing. The typology can be used individually, in small groups or at an institutional level. It has been designed to be adaptable and includes space for additional co-creation variables and responses to be added. Informal feedback from using the typology suggests it has the potential to be (a) a planning tool, (b) a reflective tool, and (c) a mapping tool.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 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".