A Comparison of Virtual Communication Software on Perceived Engagement, Enjoyment, Social Connectedness, and Willingness to Communicate in a Virtual Undergraduate Classroom
Bibliographic record
Abstract
Due to the COVID-19 pandemic, many in-person classes shifted to emergency remote learning, with Zoom being the adopted software choice of communication for many schools. Despite its accessibility, students have expressed fatigue and challenges with conversations using Zoom. Additionally, emergency remote learning has led to decreased interpersonal interactions. Thus, we wondered whether Gather.town, a teleconferencing software with proximity-chat features, might lead to a more positive learning environment than Zoom. We utilized both software in an inquiry and problem-based course, Science of Fictional Characters, at McMaster University during the Fall 2021 semester. We surveyed students’ preference between Gather.town and Zoom across five different domains: engagement, enjoyment, social connectedness, ease of use, and willingness to communicate using each software. Twelve out of 30 enrolled students responded to the survey. Participants reported more engagement (58%), higher enjoyment (58%), and greater social connectedness (92%) to their peers using Gather.town compared to Zoom, despite participants also reporting Zoom being easier to use (92%). The results further revealed no preference towards either software in terms of willingness to communicate. Although our sample size is small, our results nonetheless suggest a software with proximity chat features such as Gather.town could be a potential alternative to Zoom for fostering positive learning environments.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".