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Record W4404658747 · doi:10.18357/otessac.2024.4.1.360

Exploring Playful Hybrid Teaching Practices in Higher Education

2024· article· en· W4404658747 on OpenAlexaffvenueabout
Sandra Abegglen

Bibliographic record

VenueThe Open/Technology in Education Society and Scholarship Association Conference · 2024
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCreativityHigher educationPedagogyBest practiceSociologyPsychologyPolitical science

Abstract

fetched live from OpenAlex

The Playful Hybrid Higher Education project [https://playhybrid.education/], based in the School of Architecture, Planning and Landscape at the University of Calgary (Canada) and funded by the Imagination Lab Foundation, explores how play and creativity can be integrated into hybrid classrooms, particularly in response to the shift to blended learning during COVID-19. Through interviews and surveys with Canadian higher education stakeholders, the project aims to provide urgently needed best practice guidance to enhance staff competency and improve student outcomes. This paper presents the preliminary outcomes of the project, with a specific focus on (re)imagining a creative blended university that fosters student success. By sharing initial findings, the paper offers inspiration on how playful and creative approaches can enrich the hybrid learning experience, ultimately contributing to a more engaging and effective higher education environment.

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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.010
Scholarly communication0.0110.005
Open science0.0020.011
Research integrity0.0020.002
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 head0.296
GPT teacher head0.448
Teacher spread0.152 · 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 designQualitative
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

Citations0
Published2024
Admission routes3
Has abstractyes

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