Investigating the Predictive Factors of Secondary Education Mathematics Performance
Bibliographic record
Abstract
Several socioeconomic, environmental, ethnic, family, and educational factors influence an individual's academic performance and can determine their school performance in mathematics. Mathematical competence is one of the skills that allow students to build visions of the future from performance in the present. However, the perception that students have of mathematics, in addition to the teacher–student relationship, the classroom, gender, teaching–learning, and motivation are crucial factors for achieving an optimal academic performance and preventing school failure. The aim of the present study was: (1) to examine which variables of the dimensions "Learning Mathematics" and "School Environment" significantly contribute to the marks in the second quarter and quantify their relative importance; (2) to determine the optimal algorithm model for predicting the maximum gain in students' marks in the second quarter and quantifying it; and (3) to analyze the maximum gain in terms of gender. A total of 2018 high school students in Melilla were included in this cross-sectional study. Mathematical learning and the school environment were assessed using a validated 14-item questionnaire. Gain lift was employed to quantify the improvement in students' performance. The role of the classroom and teacher–student relationship had a greater influence on mathematics scores than affinity indicators, teaching, study time, teaching resources used, study aids, and motivation
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 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 teacher head, 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".