Parental Engagement and Pupils’ Attitude: Its Relationship to Mathematics VI Performance
Bibliographic record
Abstract
This study aimed to determine the parental engagement, pupils attitude and academic performance in Mathematics of Southwest II-District Elementary School, Cagayan de Oro City, this School Year 2022-2023. Specifically, this paper sought to do the following: the extent of parental engagement in terms of home engagement and school engagement the extent of pupils attitude towards mathematics in terms of mathematical value and mathematics enjoyment to find out the level of pupils performance in mathematics as measured by their second quarter grade and to determine the significant relationship between the pupils performance and parental engagement and pupils attitude. Questionnaire checklist was the main tool used in generating the necessary data of the study. Correlational design was used in the study. Mean, standard deviation, and Pearson Coefficient of Correlation were the statistical treatment employed in interpreting the data. The parents were highly engaged at home and at school on the academic undertakings of their child. The pupils had a very high positive attitude towards mathematics and their overall performance was satisfactory. Parental engagement and mathematical value as construct of attitude had no significant association with the pupils performance in mathematics. On the other hand, mathematical enjoyment as construct to attitude was statistically associated with their performance in mathematics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".