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Record W4380486380 · doi:10.5539/hes.v13n3p54

How to Support Co-creation in Higher Education: The Validation of a Questionnaire

2023· article· en· W4380486380 on OpenAlexvenueno aff
Miranda de Hei, Inge Audenaerde

Bibliographic record

VenueHigher Education Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsProcess (computing)Principal (computer security)Co-creationPsychologyHigher educationQuestionnaireField (mathematics)Knowledge managementCollaborative learningComputer-assisted web interviewingAccountabilityMedical educationPedagogyComputer scienceSociologyBusinessPolitical scienceMedicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.146
GPT teacher head0.466
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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