Symposium 3: What is it like for a learner to participate in a Zoom Breakout Room session?
Bibliographic record
Abstract
Though virtual classrooms are not new, the COVID-19 pandemic sent many teachers and students online for the first time. This paper examines the use of a web-based video conferencing tool, Zoom, and in particular, the use of breakout rooms as part of a student’s learning experience. We ask: what is it like for a learner to participate in a Zoom Breakout Room session? Using Max van Manen’s (2016) phenomenology of practice, we collected learners’ lived experience descriptions of participating in a Zoom breakout room, then reflected on them phenomenologically as a way to generate new insights into this recently common online learning experience. Four moments are portrayed: a learner’s arrest at the announcement of breakout rooms; a learner’s transition into a breakout room as existential suspension; surveilling self and others in a breakout room; and exiting the breakout room as a moment of foreclosure and re-disorientation. The paper compares Zoom breakout rooms with aspects of video-gaming and notices a detriment to Freirean problem-posing education if students can avoid standing, unmediated, behind just their words, even in the relative safety of a small group of peers.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".