ZOOMing into a Community: Exploring Various Teaching Practices to Help Foster Sense of Community and Engagement in Emergency Remote Teaching
Bibliographic record
Abstract
Educators across the world have been forced to shift their courses online due to the COVID-19 pandemic. As face-to-face courses become online courses during this unprecedented time, instructors are thrown into emergency remote teaching (ERT). Where online learning involves “experiences that are planned from the beginning and designed to be online, emergency remote teaching (ERT) is a temporary shift of instructional delivery to an alternate delivery mode due to crisis circumstances…[which], will return to that [original] format once the crisis or emergency has abated” (Hodges, Moore, Lockee, Trust, & Bond, 2020, para 13). The instructional demands of ERT can be overwhelming in that many instructors are trying to navigate new online teaching approaches to ensure their students have a sense of community (SoC), that is a sense of belonging and interactivity, and are still engaged, motivated, and involved in the course. Zoom, a cloud-based video conferencing platform, has boomed in popularity becoming the go-to tool many instructors use to host, facilitate, and integrate within their course, as well as to ensure a SoC is fostered and maintained. Guided by the social constructivism theory and community of inquiry (CoI) model, this quick hits piece, aims to answer the question: In what ways might Zoom foster and sustain a SoC community in ERT?
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".