Breakout with Zoom: Mixed-methods Research Examining Preservice Teachers’ Perceptions of Breakout Room Interactions
Bibliographic record
Abstract
Challenges to teacher education due to COVID-19 are widespread. Preservice teachers, in particular, have faced numerous obstacles as a result. While remote teaching became common in higher education, home-based videoconferencing became a standard means of teaching and learning. Regardless of COVID19, virtual technologies use increases within post-secondary education, progressively impacting educational experiences. Therefore, educators must consider the benefits and drawbacks of virtual online education. From a constructivist perspective, we studied preservice teachers’ interactions and perceptions of Zoom’s videoconferencing platform. Specifically, we identified preservice teachers’ interactions and responses to Zoom’s Breakout Rooms. The findings indicate that students built relationships and valued their online interactions. Additionally, males and females valued different aspects of their online interactions.We conclude with recommendations regarding videoconferences in higher education and suggest future research, including empirical studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".