Avoid Zoom Fatigue, Be Present and Learn
Bibliographic record
Abstract
This paper explores plausible reasons why some students report having more difficulty learning online, predominantly in Zoom synchronous classes, and suggests strategies that students can do to optimize their learning. During anonymous classroom observations, approximately 80% of 350 college students polled indicated it was harder to focus their attention and stay present while taking classes online. They also reported experiencing more isolation, anxiety, and depression compared to face-to-face classes, although much of this may be due to COVID-19 social isolation. Students often appear nonresponsive when attending online synchronous Zoom classes that negatively impacts the nonverbal dynamics of student–instructor interactions. Communication issues includes internet challenges, lack of facial expressions, body appearance, and movement. Students also report that it is more challenging to maintain attention, especially when they are multitasking. Suggested strategies are to optimize learning that includes arranging the camera so that you are visible, using active facial and body responses as if you are communicating to just one person face-to-face, configuring your body and environment (sitting upright and creating unique cues for each specific task), reducing multitasking and notifications, and optimizing arousal and vision regeneration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".