Evidence and Promises of AI Predictions to Understand Student Approaches to Math Learning in Abu Dhabi K12 Public Schools


Transforming the education system and building highly skilled human capital for a sustainable and competitive knowledge economy have been on the UAE’s top policy agendas for the last decade. However, in the UAE, students’ math performance on the Program for International Student Assessment (PISA) has not been promising. To improve the quality of schooling, a series of malleable predictive factors including the contributions of self-system, metacognitive skills, and instructional language skills are selected and categorized under student approaches to math learning. These factors are hypothesized as both predictors and outcomes of K12 schooling. Through the analysis using machine learning technique, XGBoost, a latent relationship between student approaches to math learning and math diagnostic test performance is uncovered and discussed for students from Grade 5 to Grade 9 in Abu Dhabi public schools. This article details how the analysis results are applied for student behavior and performance prediction, precise diagnosis, and targeted intervention design possibilities. The main purpose of this study is to diagnose challenges that hinder student math learning in Abu Dhabi public schools, uncover R&D initiatives in AI-driven prediction and EdTech interventions to bridge learning gaps, and to counsel on national education policy refinement.


Human capital, Predictive learning factors, AI prediction, The instructional core, Self-system, Metacognition, Instructional language

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