Exploring Contextual Factors Affecting Student Performance in Mathematics: A Sequential Explanatory Research
Bibliographic record
Abstract
This study aimed to explore and characterize the contextual factors that affect academic performance in learning mathematics among high school students. The study employed an explanatory sequential mixed method research design, and primary data were collected with the aid of the adopted questionnaire for quantitative data, and the interview was done for qualitative data. The study used descriptive and inferential statistical methods in analyzing and interpreting the gathered data. In addition, qualitative data were analyzed through a thematic analysis approach. Results showed that the performance of students in mathematics is influenced by different factors such as students' attitudes towards mathematics, self-efficacy, parental support, and the learning environment. In addition, a thematized interview with the students supports the quantitative analysis that their performance in mathematics was governed by the said factors. Conclusively, students must be supported in their learning by providing doable tasks and exciting problems in mathematics to boost their attitude and self-efficacy. Parents are also advised to give them the appropriate support to motivate them in their studies. Moreover, teachers must integrate welfare and positive attitudes towards students to have a conducive learning experience.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".