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Identifying the post-pandemic determinants of low performing students in Latin America through Interpretable Machine Learning methods

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Pith's one-line read Using stacked ML models and SHAP values on PISA 2022, the paper ranks the correlates of bottom and low performance in 10 Latin American countries, with grade repetition, family SES, and ICT access as the most consistent top factors.

arxiv 2509.24508 v2 pith:AELWON6J submitted 2025-09-29 econ.GN q-fin.EC

Marcos Delprato This is my paper
classification econ.GNq-fin.EC
keywords studentcountrieslatinlearningamericacontributedeterminantsfactors
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper uses data from the 2022 PISA tests, which measure 15-year-olds' reading, math, and science skills, to study the lowest-performing students in ten Latin American countries. The author defines two groups: students who reached the very bottom level (level 0) compared with students one step above (level 1), and students at level 1 compared with students who reached level 2, the basic competency threshold. Students who score below level 2 are called low performers.

To find which factors separate these groups, the author trains five machine learning models: logistic regression, lasso, gradient boosting, random forests, and neural networks. The predictions are combined in a stacked ensemble. Then a technique called SHAP is used to rank how much each factor contributes to the model's prediction for each student. The top factors are averaged across all students to get a global ranking, and the author also examines two specific students at the extremes of the SHAP distribution: the one most likely to fall into level 0 and the one most likely to reach level 1.

The results show that repeating a grade in primary school, low family wealth, having few digital devices at home, and speaking a minority language at home are consistently associated with being at the very bottom. School factors such as bad school climate, weak ICT infrastructure, and poorly prepared teachers also matter. The country-level analysis finds similar top factors across most countries.

Extended reading notes

Core claim

The paper's central claim is that repetition at primary, household wealth, and educational ICT inputs are the top-ten ranked covariates in at least 8 out of 10 countries, and that a student at the highest risk of being a level 0 achiever 'speaks a minority language and had repeated, has no digital devices at home, comes from a poor family and works for payment half of the week' (Section 5.3.1, Figure 4a, and Conclusions). More broadly, the paper claims that SHAP analysis of stacked ML models identifies the principal determinants of learning poverty in the LAC region.

Load-bearing premise

The paper assumes that SHAP values computed with LinearSHAP on the stacked classifier give valid feature attributions for the original student, family, and school covariates. The stacked model is a weighted linear combination of predicted probabilities from gradient boosting, random forest, lasso, and neural networks, none of which is linear in the raw features. LinearSHAP (Eq. 14, Appendix B) assumes f(x) = beta X + b; the paper does not justify applying it to the stacked model, so the reported feature rankings may not reflect true marginal contributions of the covariates.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The paper introduces no new free parameters, axioms, or entities beyond standard ML hyperparameters (tuned by cross-validation) and the domain assumptions inherent in using PISA data and SHAP attributions.

assumptions (2)
  • domain assumption PISA 2022 achievement levels are comparable across the 10 countries and reflect learning competency.
    Section 3 relies on PISA levels as the outcome; no cross-country comparability checks are performed.
  • domain assumption SHAP feature attributions on the fitted model can be interpreted as determinants of student performance.
    Section 4.4 and the Discussion imply that SHAP rankings identify 'drivers' of poor performance; this assumes the model captures relevant causal structure.

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Pith. "Pith review of Identifying the post-pandemic determinants of low performing students in Latin America through Interpretable Machine Learning methods." pith.science (2026). https://pith.science/paper/AELWON6J

@misc{pith2026250924508,
  author       = {Pith},
  title        = {Pith review of: Identifying the post-pandemic determinants of low performing students in Latin America through Interpretable Machine Learning methods},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AELWON6J}},
  note         = {Machine review of arXiv:2509.24508}
}
read the original abstract

Introduction. The high prevalence of students not achieving basic learning competencies in Latin America (LAC) is concerning, even more so considering the region's deep structural inequalities and the larger post-pandemic learning losses. Within this scenario, the paper aims to contribute to the identification of the determinants of bottom and low performers (below level 2). Methodology. Based on 2022 data from the Programme for International Student Assessment (PISA) for 10 LAC countries, and using a stacking model integrating binary classification models as well as by applying Shapley Additive Explanations (SHAP) analysis for interpretability, we identify critical factors impacting on the student performance across low performers groups. Results. We find that a student with the highest probability of being a not achiever speaks a minority language and had repeated, has no digital devices at home, comes from a poor family and works for payment half of the week, and the school the student attends has wide disadvantages such as bad school climate, weak Information and Communication Technology (ICT) infrastructure and poor teaching quality (only a third of teachers being certified). For countries' estimates, we find quite homogeneous patterns regarding the contribution of top ranked factors, with repetition at primary, household wealth, and educational ICT inputs being top ten ranked covariates in at least 8 out of the 10 total countries. Discussions. The paper findings contribute to the broad literature on strategies to identify and to target those most left behind in Latin American education systems.

Figures

Figures reproduced from arXiv: 2509.24508 by the authors.

Figure 1
Figure 1. Analytical steps 20 [PITH_FULL_IMAGE:figures/full_fig_p020_1.png] view at source ↗
Figure 2
Figure 2. Area under the ROC curve, different ML models [PITH_FULL_IMAGE:figures/full_fig_p021_2.png] view at source ↗
Figure 3
Figure 3. Model: level 0 versus level 1 (bottom performing group). Beeswarm dot plot and heatmap of individual [PITH_FULL_IMAGE:figures/full_fig_p022_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Model: level 0 versus level 1 (bottom performing group). Student [PITH_FULL_IMAGE:figures/full_fig_p023_4.png]
Figure 5
Figure 5. Figure 5: Model: level 1 versus level 2 (low performing group). Student [PITH_FULL_IMAGE:figures/full_fig_p024_5.png]
Figure 6
Figure 6. Figure 6: Frequency of student/family and school covariates with countries’ SHAP values appearing in the top 10 ranked estimates Notes: (1) See Appendix C for variables’ acronyms and definitions. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_6.png]

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Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.