Bibliographic record
Abstract
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
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".