Students’ Attitudes to Online Learning by Means of Zoom in the Period of the COVID-19 Crisis
Bibliographic record
Abstract
This study examined students’ attitudes to characteristics of learning in Zoom, attitudes to the quality of teaching in Zoom and ways of learning, about a year after the outbreak of the COVID-19 crisis. The aim of the current study was to examine, What are students’ attitudes to the characteristics of learning in Zoom, the quality of teaching in Zoom and ways of learning in Zoom? Facilitators, inhibitors, implications and recommendations were identified. The study is a quantitative one, the questionnaire contained closed questions, and 712 students who study in higher education institutions in Israel participated in the study. The findings showed that most students are satisfied with learning in Zoom, and that there was a significant improvement in the students’ attitudes towards learning in Zoom during their studies in the Covid-19 period. It was found that older students have more positive attitudes towards learning in Zoom, and learning disorders are connected to more negative attitudes towards learning in Zoom; however, there was also an improvement in the attitudes of students with learning disorders, during their learning experience in the COVID-19 period.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".