Explainable AI Reveals What Really Predicts Math Achievement Across Ten Countries

A new study has applied explainable artificial intelligence to one of the largest educational datasets in the world, and the results offer both a sobering confirmation and a methodological wake-up call for comparative education research. Working with mathematics achievement data from 74,235 fifteen-year-olds across ten purposively selected education systems, researchers Liu Liu of the University of Georgia and Rui Dai of Arizona State University built a survey-weighted, plausible-value-aware machine-learning workflow designed specifically for the complexities of the Programme for International Student Assessment, or PISA. Their analysis, published in Large-scale Assessments in Education, demonstrates that when the technical realities of large-scale assessment design are taken seriously, a boosted tree model consistently outperforms conventional linear benchmarks, and the predictors that matter most are strikingly stable in some respects and surprisingly country-specific in others.

The ten systems examined were Argentina, Chile, Chinese Taipei, Finland, Hungary, Italy, Japan, Korea, the Philippines, and the United States. The authors stress that this was a purposive comparative sample, chosen to span geographic regions, OECD membership status, and a wide range of average performance, from a weighted mean mathematics score of 352.57 in the Philippines to 531.14 in Chinese Taipei. The design supports comparisons across these ten systems but does not license claims about all countries or continents. Within the sample, 2,617 schools contributed students, with country sample sizes ranging from 4,838 in the United States to 11,975 in Argentina.

What distinguishes this study from much of the educational data-mining literature is its fidelity to PISA’s assessment architecture. Mathematics achievement in PISA is not a single number but a set of ten plausible values, multiple imputed estimates that represent uncertainty in each student’s latent proficiency. The researchers fitted and evaluated every model across all ten plausible values rather than relying on just the first. They also incorporated the final student weight, W_FSTUWT, in descriptive statistics, model fitting, and test-set evaluation, and used the 80 student replicate weights to estimate sampling variability. Total variance for each performance metric combined the mean replicate-weight sampling variance with the between-plausible-value variance, following the formula U-bar plus (1 + 1/M) times B, so that confidence intervals reflected both complex sampling uncertainty and achievement-scaling uncertainty. Few machine-learning studies of PISA data go to these lengths.

The predictor side of the analysis was equally disciplined. The authors constructed a transparent primary candidate pool of 33 predictors from PISA 2018 student questionnaire variables and OECD-derived indices, organized into six substantive domains: student demographics, family socioeconomic status and home resources, engagement and learning time, peer climate, belonging and parent support, and classroom climate. Rather than assuming all 33 variables mattered everywhere, they performed country-specific stability feature selection using only training schools. Three complementary methods were applied: mutual information, which captures general dependence; weighted elastic net regression, a regularized linear method that handles correlated predictors through combined L1 and L2 penalties; and weighted random forest ranking, which captures nonlinear and interaction-based structure. Feature rankings were repeated across ten plausible values and five internal school-level folds, yielding 50 selection runs per country. A predictor was deemed stable if selected in at least half of those runs. The resulting stable sets contained between 11 and 15 predictors, averaging 14.0 per country.

Four models were then compared under country-specific school-level holdout evaluation, with roughly 70 percent of students in training and 30 percent in test sets. Splitting by school rather than by student reduced leakage from students in the same school appearing on both sides. The contenders were weighted linear regression, weighted linear regression augmented with pairwise interactions, random forest, and CatBoost, a gradient-boosted tree ensemble designed for structured tabular data. Averaged across countries, CatBoost was the clear winner, achieving a mean weighted R-squared of 0.358 and a mean mean absolute error of 57.29 score points. Random forest followed with a mean weighted R-squared of 0.313 and an MAE of 59.30. The interaction-augmented linear model barely improved on the additive linear benchmark, with mean R-squared values of 0.290 versus 0.286. CatBoost posted the lowest weighted MAE in all ten systems, and its advantage over random forest was consistent but moderate: roughly 2.01 MAE points and 0.045 in R-squared on average. Its country-level R-squared ranged from 0.210 in Italy to 0.443 in Hungary.

The interpretive core of the study used SHAP, or Shapley additive explanations, a technique that decomposes each model prediction into additive contributions from individual features. The authors were careful to frame SHAP summaries as model-based explanations of predictive associations, not causal effects. Because CatBoost performed best, interpretation focused on its SHAP values, computed on held-out test schools and averaged across all ten plausible values to produce PV-robust stability summaries. The headline finding is that books at home was the most stable predictor of all, ranking among the top ten SHAP predictors in all ten countries on average and among the top five in 8.7 countries on average. Highest parental occupational status was nearly as stable, appearing in the top ten in 9.2 countries and the top five in 7.1. Socioeconomic status itself appeared in all ten country-specific models and ranked in the top ten in 8.3 countries on average, though its average top-five count was lower at 4.3.

Beyond the socioeconomic core, the picture became more heterogeneous. Grade placement entered the stable feature sets of seven countries and ranked in the top ten in all seven, but it was not selected in Chinese Taipei, Japan, or Korea. Mathematics learning time appeared in six countries and ranked in the top ten in all six, with an average top-five count of 5.5, and it ranked especially highly in Chinese Taipei, Japan, and the United States. Test effort was retained in only four systems but was consistently influential there, ranking in the top five in all four and even first in Finland, Italy, and Korea in the diagnostic visualization. Directed instruction and disciplinary climate also showed cross-national reach, ranking in the top ten in 7.0 and 5.8 countries on average respectively. The authors argue that this mix of stability and country-specificity is precisely why a single pooled importance ranking would be misleading.

Sensitivity analyses reinforced the robustness of these conclusions. Comparing SHAP summaries based on the first plausible value with summaries averaged across all ten showed only small differences, with maximum absolute gaps of 0.8 countries for top-ten counts, 1.2 countries for top-five counts, and 1.25 rank positions for mean rank, confirming that the main pattern was not an artifact of PV1MATH. A second sensitivity analysis removed grade placement from the stable feature sets where present and refitted the CatBoost models. Performance dropped modestly, with a mean R-squared change of -0.028 and a mean MAE increase of 1.17 score points, the largest decline occurring in Chile, where R-squared fell by 0.091 and MAE rose by 3.92 points. Crucially, the SHAP stability pattern remained broadly similar without grade: books at home, parental occupational status, learning time, socioeconomic status, test effort, directed instruction, and disciplinary climate all stayed among the most stable predictors.

The authors are explicit about the limits of their contribution. They do not claim to have discovered new determinants of mathematics achievement; domains such as socioeconomic resources, home literacy environments, learning time, and classroom climate are already well established. The value lies in showing how these familiar predictors behave under a rigorous, country-specific, survey-weighted, plausible-value-aware explainable machine-learning framework, and in separating three questions that are often conflated: which models predict best, which predictors are stable across systems, and which are context-specific. They also caution that the analysis is predictive rather than causal, that the ten-system sample is not statistically representative of global education, that the 33-predictor pool cannot exhaust all relevant influences such as school policies or teacher characteristics, and that cross-national questionnaire comparisons may be affected by response styles, translation, and measurement comparability.

The implications reach well beyond this dataset. The framework, with its train-only feature selection, school-level holdout evaluation, replicate-weight variance estimation, and all-plausible-value pooling, offers a reproducible template that could be extended to more PISA systems, to other assessments such as TIMSS and PIRLS, to school-level and system-level predictors, and to fairness analyses examining whether prediction errors or SHAP patterns differ across gender, socioeconomic, immigrant, or language groups. Repeated-cycle analyses could test whether cross-national stability patterns persist as PISA evolves. For a field increasingly drawn to black-box prediction, the study makes a compelling case that explainability and methodological rigor are not optional extras but the foundation of responsible machine learning in comparative education.

Subject of Research: Explainable machine learning applied to predicting and interpreting mathematics achievement in PISA 2018 across ten education systems.

Article Title: Explainable AI for predicting and interpreting mathematics achievement: a cross-national analysis of PISA 2018

Article References: Liu, L., & Dai, R. (2026). Explainable AI for predicting and interpreting mathematics achievement: a cross-national analysis of PISA 2018. Large-scale Assessments in Education, 14(1), Article 46. https://doi.org/10.1186/s40536-026-00320-y

Image Credits: AI Generated

DOI: 10.1186/s40536-026-00320-y

Keywords: PISA 2018, mathematics achievement, explainable artificial intelligence, SHAP, CatBoost, machine learning, survey weights, plausible values, large-scale assessment, socioeconomic status, comparative education, feature selection