Online Tools for Enhancing the Family’s Role in Student Educational Achievement
Bibliographic record
Abstract
The use of technology in education has been widely investigated, with a particular focus on remote communication during the COVID-19 pandemic. These settings have prompted a deeper study of the role that families can play in remote learning to support student educational achievement. This study has explored the student–family factors that could influence student educational achievement, building a theoretical framework of those factors and proposing associated online tools. Accordingly, this study used a cross-sectional survey design to explore the opinions and beliefs of parents about those factors and the suggested online tools based on the theoretical framework. The study sample consisted of 1,259 parents who responded to a survey of 10 items related to student–family factors and the suggested associated online tools. The findings indicated that many factors could improve student educational achievement, especially for students from underprivileged families. This study recommends providing online discussion tools to serve as a communication channel between schools and the families of students, supporting synchronous or asynchronous learning platforms, ensuring accessibility for all families who are involved with the school, and facilitating the school’s timely engagement with families through the designated online discussion tools.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".