A classroom divided: A mixed-methods evaluation of hybrid and virtual teaching and learning at a small liberal arts university college.
Bibliographic record
Abstract
Hybrid teaching and learning, in which instructors teach to students in person and virtually simultaneously, has emerged as an increasingly popular option for post-secondary educational institutions. We report a mixed-methods evaluation of hybrid and purely virtual teaching and learning models in two sections of an upper-level undergraduate social work course at a small Canadian liberal arts university college. Students completed a quantitative survey assessing a variety of outcomes associated with the course. Research assistants also observed classroom behaviours and interactions across three weeks of classes in each section. The evaluation identified a wide range of costs associated with the hybrid model and limited benefit when compared against a purely virtual option. Students in the hybrid section reported lower perceived control over their learning, less perceived value gained from the course, and increased boredom throughout the course. Technical issues, while occurring in both sections, were far more disruptive in the hybrid section. Finally, in-person and virtual students were disconnected from each other and often used “othering” language to describe students using a different attendance modality. We provide tentative recommendations for changes to the traditional hybrid model in the context of small liberal arts university 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 | 0.041 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".