Predicting the Mathematics Literacy of Resilient Students from High‐performing Economies: A Machine Learning Approach
Why this work is in the frame
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Bibliographic record
Abstract
Mathematics is a crucial yet challenging subject for all students. Therefore, it is important to understand the role of academic resilience in mathematics, which enables students to overcome academic challenges. This study applied two machine learning algorithms, Lasso Regression (LR) and Random Forest (RF), to predict the mathematics literacy of resilient students from high-performing economies across cultures in PISA 2022. The findings indicated both RF and LR performed better in Western cultures than in Eastern cultures. Furthermore, in Eastern cultures, mathematics self-efficacy for 21st-century skills played an important role in predicting resilient students’ mathematics literacy, followed by self-efficacy towards mathematics, and mathematics anxiety. In Western cultures, self-efficacy towards mathematics was the predominant predictor, followed by mathematics self-efficacy for 21st-century skills. Theoretically, this study identifies key factors in predicting resilient students’ mathematics literacy across cultures. Methodologically, it is the first to apply ML in exploring resilient students’ mathematics literacy. Practically, it guides educators interested in developing interventions to improve resilient students’ mathematics literacy. • Lasso Regression (LR) and Random Forest (RF) predicted the mathematics literacy of resilient students from high-performing economies. • Findings indicated both RF and LR performed better in Western cultures than in Eastern cultures. • In Eastern cultures, mathematics self-efficacy for 21st-century skills played an important role . • In Western cultures, self-efficacy towards mathematics was the predominant predictor.
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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 | 0.002 | 0.001 |
| 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 | 0.000 | 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