Predictive Analysis on Students’ Academic Performance in Mathematics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it