Multimedia Showdown: A Comparative Analysis of Audio, Video, and Avatar-Based Communication
Bibliographic record
Abstract
Our new work culture relies heavily on online meetings and computer mediated communication (CMC). However, making an online meeting engaging while keeping communication productive is a major challenge. We collected quantitative data from the user engagement scale (UES) and qualitative data from semi-structured interviews to investigate how user engagement differed. Using the gamified web-conferencing platform Gather, we compared four communication channels: (1) audio-only, (2) audio and video (no avatar), (3) audio and avatar (no video), and (4) audio and video and avatar. We began qualitative data analysis using reflexive thematic analysis. Although the UES results did not reveal significant differences, the preliminary results from the thematic analysis such as people prefer communication platforms designed for specific use cases because video makes them feel more self-conscious, while avatars make them feel more represented. Lastly, we provide a work-in-progress applied definition of user engagement in communication channels with their perspective on individual engagement constructs.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".