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Record W6929991553 · doi:10.5281/zenodo.10917808

Playful Hybrid Education: Surveys

2024· report· en· W6929991553 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typereport
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene Regulatory Network Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBachelorPerceptionSurvey data collectionHigher educationSurvey researchSurvey instrumentFocus group

Abstract

fetched live from OpenAlex

The Playful Hybrid Higher Education project explores faculty and student experiences in the hybrid classroom to develop guidance for educators on the emerging education model, with a focus on playful and creative pedagogy. To develop appropriate guidance, two surveys were conducted. Survey One asked about experiences with hybrid teaching and learning. The survey served as the initial step in understanding perceptions of hybrid education, focusing on attitudes and experiences of both faculty and students. The survey targeted education professionals and learners at Canadian Higher Education institutions. It was conducted online. Survey Two was aimed at undergraduate students in the School of Architecture, Planning and Landscape (SAPL)), entering the Bachelor of Design in City Innovation (BDCI) program, although it was also open to other students at the University of Calgary. The online survey invited students to articulate their experiences of different learning modes: in-person, online and blended. Participation in the survey was completely voluntary, with no personal data collected. Completion of the survey took approximately five minutes, depending on the length of the answers provided. This report presents the survey results. Editorial Team: Sandra Abegglen, Fabian Neuhaus, Mia Brewster Organization: School of Architecture, Planning and Landscape, University of Calgary Grant: Imagination Lab Foundation Project website: https://playhybrid.education

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.833
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.006

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 head0.029
GPT teacher head0.273
Teacher spread0.243 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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 routes2
Has abstractyes

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