Using machine learning algorithms to predict students' general self-efficacy in PISA 2018
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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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".