Parental Involvement and Academic Performance of Learners
Bibliographic record
Abstract
The primary educators of their children are their parents. Their support impacts childrens growth, learning, and later academic results. This study sought to identify parental involvement in learners academic performance in terms of Parenting, Communication, Volunteerism, Learning at Home, Decision Making, and Community Collaboration, find the level of academic performance of learners in the second quarter and significant relationship of the parental involvement and academic performance. This research was conducted to 232 parents in the East 2 District, Division of Gingoog City. The respondents were selected through Slovins formula. This study utilized the questionnaire from the study of Eldeeb (2018). The study utilized a descriptive correlation research design which used frequency, percentage, mean, and standard deviation. Pearson Product Moment Correlation was used to determine the significant relationship between the variables. Results revealed that parents are involved in their childrens school life, but this does not seem to have a significant impact on their academic performance. The lack of a significant correlation between parental involvement and grades indicates that parents efforts are not always reflected in their childrens performance. Thus, this study recommends that parents will monitor their childrens progress to maximize the effect of their involvement in their childrens academic performance. Future research is also recommended on how parental involvement can impact other aspects of learners academic performance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".