Analysis of the Effects of World Bank Macroeconomic and Management Indicators on Sustainable Education Quality on PISA Scores Using the SHAP Explainable Artificial Intelligence Method - Kisman, Zulfukar Aytac; Kan, Ayse Ulku; Uzun, Selman; Kan, Mehmet Alper; Yildirim, Gungor (Mdpi, 2026)
This study proposes a multi-objective, multi-class explainable modeling framework to explain country performance profiles in PISA Mathematics (PISAM), Reading (PISAR), and Science (PISAS). Instead of treating PISA as a simple ranking, the study models each country's Low/Medium/High-achieving class and asks which structural signals the model relies on when assigning a country to this class. To this end, the study combines governance quality (e.g., accountability, control of corruption, and political stability, etc.), economic and administrative capacity, and regional/institutional location in a single prediction pipeline and explains the resulting classifications with SHAP contributions conditional on class. While the findings do not point to a single, universal determinant, in mathematics, high-level profiles cluster around political stability, economic scale barriers, and regional location, along with governance indicators; in reading, economic capacity is explicitly integrated into this institutional core; and in science, in addition to these two dimensions, the shared institutional dynamics of regional blocs come into play. Furthermore, the study not only produces explanations but also quantitatively reports their reliability. The fit with the model output (Fidelity) and the traceability of the decision logic (Faithfulness) are 0.95/0.85 for PISAM, 0.89/0.92 for PISAR, and 0.89/0.89 for PISAS, which demonstrates high internal consistency and traceability of the decision process. Overall, the study reframes the PISA results not as isolated test scores but as structural profiles generated by the combination of governance, capacity, and region, revealing the policy-relevant levers behind high performance as a transparent and reproducible decision-making pipeline. This provides policymakers with an important roadmap for creating a sustainable education policy.
Keywords: World Bank; governance indicators; PISA; sustainability; XAI; SHAP