Predictive Analysis on Students’ Academic Performance in Mathematics
Bibliographic record
Abstract
This research aimed to determine the predictors of academic performance in mathematics of Grade 10 students using descriptive correlational design. The respondents were 435 Grade 10 students from the three identified public high schools in Lapu-Lapu City and Liloan, Cebu, Philippines. A survey questionnaire was used to describe student-related factors, teacher-related factors, and environment-related factors while the First Quarter Grades were used to measure students’ academic performance in mathematics. Data gathered were treated statistically using frequency count, percentage, weighted mean, and multiple regression. The results showed that most of the respondents were 14 to 15 years old and were female; most of the parents were high school graduates and had a combined family monthly income of 10,000 pesos and below. The respondents had satisfactory performance. Also, teaching skills and instructional materials used by the teacher are significant predictors of academic performance in mathematics. However, the students’ interest, study habits, teacher’s personality, school environment and home environment of the students were not significant predictors of the mathematics performance of the students. It was concluded that the teacher-related factors as to teaching skills and instructional materials used can predict the academic performance of the students. The researchers recommended that the proposed intervention plan could be utilized and monitored.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".