Exploring Parental Experiences With School-Aged Children Receiving Web-Based Learning: Cross-Sectional Study
Bibliographic record
Abstract
Background: Web-based learning has transformed education. Its ability to overcome physical barriers and deliver knowledge at the click of a button has made web-based learning popular and ensured that it will continue to be used in the future. The involvement of parents in web-based learning is fundamental to the success of the educational process, but limited attention has been paid to the impact of web-based learning on parents. Objective: This study examined parental experiences with school-aged children receiving web-based learning in Jeddah, Saudi Arabia. Methods: We sent cross-sectional, anonymous web-based questionnaires to school-aged children's parents. A total of 184 parents completed the survey. Results: Parents' negative experiences of web-based learning (mean 4.13, SD 0.62) exceeded their positive experiences (mean 3.52, SD 0.65). The most negative experience reported by parents was their child's boredom due to prolonged sitting in front of a device (mean 4.56, SD 0.69). The most positive experience was their child's technological skill enhancement (mean 3.98, SD 88). Their child's lack of social interaction and friendship building promoted stress among parents (r=-0.190; P=.01). At the same time, their child's technological skill enhancement reduced stress among parents (r=0.261; P=.001). The most reported (63/184, 34.2%) obstacle to web-based learning was having multiple learners in the same household. Conclusions: Web-based learning is a fundamental learning method and will continue to be used in the future because of its ability to overcome many barriers to education. Parental involvement in the continuity and success of the web-based learning process is crucial. However, the findings of this study illustrated that parents' experiences of web-based learning were more negative than positive. Parents who reported negative experiences reported an increase in stress and faced more obstacles due to web-based learning. Thus, more attention and intervention are needed to promote positive web-based learning experiences among parents.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".