AI-based identification and support of at-risk students: A case study of the Moroccan education system

2025-04-09

AI Summary

This study addresses the delayed identification of student dropout risk and untimely interventions in Morocco’s education system by developing an interpretable AI prediction model. Methodologically, it pioneers the integration of multi-source heterogeneous national education data—including academic performance, attendance, and socioeconomic indicators—applying temporal feature engineering and SHAP-based interpretability analysis, and leveraging ensemble learning via XGBoost and Random Forest for dynamic risk assessment. Its core contribution is a lightweight, interpretable, and time-aware modeling framework specifically designed for educational contexts in developing countries. Empirical evaluation on real-world data demonstrates strong predictive performance: 88% accuracy, 88% recall, 86% precision, and 87% AUC—substantially enhancing early-risk detection capability and policy-relevant decision trustworthiness.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Student dropout is a global issue influenced by personal, familial, and academic factors, with varying rates across countries. This paper introduces an AI-driven predictive modeling approach to identify students at risk of dropping out using advanced machine learning techniques. The goal is to enable timely interventions and improve educational outcomes. Our methodology is adaptable across different educational systems and levels. By employing a rigorous evaluation framework, we assess model performance and use Shapley Additive exPlanations (SHAP) to identify key factors influencing predictions. The approach was tested on real data provided by the Moroccan Ministry of National Education, achieving 88% accuracy, 88% recall, 86% precision, and an AUC of 87%. These results highlight the effectiveness of the AI models in identifying at-risk students. The framework is adaptable, incorporating historical data for both short and long-term detection, offering a comprehensive solution to the persistent challenge of student dropout.

Problem

• AI predicts student dropout risk using machine learning
• Identifies key factors affecting dropout with SHAP analysis
• Adaptable framework tested on Moroccan education data

Innovation

• AI-driven predictive modeling for dropout risk
• SHAP explanations to identify key factors
• Adaptable framework with high accuracy metrics