Primary School Students’ Online Learning During Coronavirus Disease 2019: Factors Associated With Satisfaction, Perceived Effectiveness, and Preference
Bibliographic record
Abstract
Emergency online education has been adopted worldwide due to coronavirus disease 2019 (COVID-19) pandemic. Prior research regarding online learning predominantly focused on the perception of parents, teachers, and students in tertiary education, while younger children's perspectives have rarely been examined. This study investigated how family, school, and individual factors would be associated with primary school students' satisfaction, perceived effectiveness, and preference in online learning during COVID-19. A convenient sample of 781 Hong Kong students completed an anonymous online survey from June to October 2020. Logistic regression was conducted for 13 potential factors. Results indicated that only 57% of students were satisfied with their schools' online learning arrangement and 49.6% regarded the online learning as an effective learning mode. Only 12.8% of students preferred online learning, while 67.2% of students preferred in-person schooling. Multiple analyses suggested that teacher-student interaction during online classes was positively associated with students' satisfaction, perceived effectiveness, and preferences in online learning. Compared to grades 1-2 students, grades 3-6 students perceived more effectiveness and would prefer online learning. Happier schools were more likely to deliver satisfying and effective online education. Students who reported less happiness at school would prefer online learning, and students who reported less happiness at home would be less satisfied with online learning and reflected lower effectiveness. Teachers are encouraged to deliver more meaningful interactions to students and offer extra support to younger children during online classes. Primary schools and parents are encouraged to create a healthy and pleasant learning environment for children. The government may consider building up happy schools in the long run. The study findings are instrumental for policymakers, institutions, educators, and researchers in designing online education mechanisms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".