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Record W4387665283 · doi:10.15173/ijsap.v7i2.5280

Curriculum enhancement through co-creation: Fostering student-educator partnerships in higher education

2023· article· en· W4387665283 on OpenAlexvenueno aff
Ana Paula Cabral, Stephanie Fuller, Janet De Wilde, Khahliso Khama, Marianne Melsen

Bibliographic record

VenueInternational Journal for Students as Partners · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumCo-creationInstitutionHigher educationPedagogyQueen (butterfly)Curriculum developmentPolitical sciencePublic relationsSociologyEngineering ethicsEngineeringKnowledge managementComputer science

Abstract

fetched live from OpenAlex

This case study presents an institutional approach to curriculum enhancement and co-creation. It explores how these two elements of a university’s strategy interlink through institutional values, curriculum development initiatives, and the advent of a new recognition scheme for student co-creators at Queen Mary University of London in the UK. It explores how the delivery of curriculum enhancement projects has been made possible through co-creation with students and discusses its outcomes: curriculum enhancement resources for staff and students, recognition for students, and joint presentations and publications. This case study also reflects on the experience of student co-creators and the benefits and challenges for staff and the institution, considers the specific contexts required to promote a shift in institutional culture towards co-creation, and shares successes and recommendations for implementing this approach.

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 imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0130.014
Scholarly communication0.0140.010
Open science0.0040.032
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.228
GPT teacher head0.618
Teacher spread0.390 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2023
Admission routes1
Has abstractyes

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