Factors Affecting Academic Performance of Grade 10 Learners in Mathematics: Basis for Learning Enrichment Program
Bibliographic record
Abstract
This study aimed to determine the association and effect of various factors on the academic performance of Grade 10 learners in Pitogo District, Division of Quezon, to develop a learning enrichment program. A survey questionnaire that the researcher had designed and a data collection form for the respondents' grades for the second quarter served as the primary tools for acquiring the necessary data. One hundred ninety-seven (197) respondents were selected systematically for the study. This study employed quantitative research utilizing a descriptive-evaluative correlational design. The gathered information was examined using an arithmetic mean and percentage to determine the associated factors and level of academic performance of grade 10 students, respectively. The significant relationship between the associated factors and academic performance was statistically analyzed using Spearman’s rank-order correlation. Findings revealed that all of the factors affecting the academic performance of the students were significantly associated with each other. However, only teaching approaches and communication skills have a significant effect on their academic performance. Based on the conclusions, it was recommended that schools use the proposed learning enrichment program that will improve grade 10 students’ academic performance. Likewise, future researchers may also investigate other variables that may affect students’ learning in mathematics or other learning areas.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".