How to Support Co-creation in Higher Education: The Validation of a Questionnaire
Bibliographic record
Abstract
In this study, we regard co-creation as a collaborative process where students, lecturers and working field professionals from outside the university jointly develop innovative products, processes or knowledge. In co-creation all stakeholders equally contribute to the collaborative process and aim to create beneficial outcomes for each participant. Co-creation can be used as a valuable pedagogical method to support continuous interaction between learning and working in higher education to foster innovation. However, this process is not necessarily mastered by co-creation groups. In order to identify which components of this collaboration process can be further improved, we developed a questionnaire to assess co-creation processes in higher education. Students, lecturers and working field professionals participating in co-creation projects completed the questionnaire. We validated the questionnaire using a principal component analysis. The seven extracted scales proved to be sufficiently reliable. The final questionnaire consists of seven components: positive interdependence, individual accountability, collaboration, shared mental models, safe and supporting conditions, creative community, and group evaluation. We described how the tool can be used in practice.
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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 | 0.056 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".