Using machine learning algorithms to predict students' general self-efficacy in PISA 2018
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
Self-efficacy is a critical psychological construct that exerts a positive impact on students' learning experiences and global well-being. Previous studies explored the factors related to the development and variation of students' self-efficacy, but they only focused on a limited set of predictors. To gain a more comprehensive understanding of the factors affecting self-efficacy, it is necessary to build a predictive model based on a large number of predictors using a data-driven approach. Therefore, guided by socio-ecological theory, we categorized 256 candidate predictors from the PISA 2018 student and school questionnaires in five levels of socio-ecological systems. We then used two machine learning algorithms, Lasso and XGBoost, to predict self-efficacy of 612,004 students aged 15 to 16 years from 79 countries and regions. The results showed that XGBoost outperformed Lasso. We then extracted feature importance from the best-performing XGBoost model to rank the features both overall and within each level of the socio-ecological systems. The analysis revealed that individual-level attributes such as mastery goal orientation, meaning of life, and positive emotions were the most important predictors of self-efficacy. Other significant contextual factors included parents' emotional support, home possessions, and school climate factors (e.g., cooperation climate). Furthermore, self-efficacy varied significantly across countries. This study advances our understanding of self-efficacy by identifying the important predictors from different levels of socio-ecological perspectives. The results suggest that self-efficacy is a composite outcome shaped by a myriad of influences spanning from individual factors to broader socio-ecological perspectives.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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