Factors Affecting Mathematics Performance: Basis for an Intervention Plan
Bibliographic record
Abstract
Mathematics, being a highly advanced field of Science, is closely linked to success in modern society as it is considered a necessary skill. This study sought to determine the extent to which attitudes, parental influence, and self-efficacy are factors that affect learners' performance in Mathematics; to detect learners' Mathematics performance throughout the First Quarter of the School Year 2023 - 2024; to determine the significance of the relationships between factors affecting performance in Mathematics and learners’ First Quarter Mathematics performance of the School Year 2023 -2024; and to find out which of the independent variable/s singly or in combination best predict/s performance in Mathematics; and also to create an intervention plan based on the study's findings. There were one hundred seventy-six (176) Grade 6 learners from the District of Laguindingan schools, Division of Misamis Oriental that participated in the survey. The instrument used was adapted and modified from Peteros et al. (2019), Silao (2018), and Dagdag et al. (2020). The data gathered were analyzed using frequency, percentage, mean, standard deviation, Pearson Moment Correlation, and multiple regression analysis. The findings of the study showed that attitude toward Mathematics is the best indicator of Mathematics performance. The researcher recommends that the DepEd officials, administrators, parents, and stakeholders work together to deal with learners' Mathematics performance. Teachers may conduct parent workshops or training sessions and counseling on how to set realistic, achievable goals based on the child's capabilities.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".