Early Warning System for Student Dropout Risk Using Behavioral Analytics, Explainable AI, and Hybrid Machine Learning Models

Abstract

Student dropout and academic underperformance remain critical challenges in higher education institutions. Traditional attendance-based monitoring systems fail to provide early warning signals for identifying at-risk students. This paper proposes an AI-driven Early Warning System (EWS) that integrates behavioral analytics, IoT-based attendance data, and hybrid machine learning models for accurate student risk prediction. The proposed system utilizes Random Forest, XGBoost, and LSTM models to analyze multi-dimensional student data and classify students into risk categories. To enhance transparency, Explainable AI (SHAP) is employed to interpret model predictions and identify key contributing factors. A dynamic risk scoring mechanism is introduced for real-time student monitoring. Experimental results demonstrate that the proposed system achieves 94% prediction accuracy, outperforming traditional methods. The system enables early intervention, improves student retention, and supports data-driven academic decision-making.

Introduction

This study proposes an AI-powered Early Warning System (EWS) to identify students at risk of dropping out in higher education. Student retention remains a major challenge, as many students leave their courses due to poor attendance, low engagement, and declining academic performance. Traditional monitoring systems mainly record attendance and fail to detect early warning signs. The proposed system addresses this issue by combining attendance, behavioral, and academic data with advanced AI techniques to predict dropout risk and support timely interventions.

The literature review highlights that previous research has used machine learning models such as Decision Trees, Support Vector Machines (SVM), Random Forest, XGBoost, and LSTM for student performance prediction. However, most existing systems have three key limitations: limited behavioral analytics, lack of real-time risk classification, and poor explainability. To overcome these challenges, the study incorporates behavioral analysis and Explainable AI (XAI) using SHAP (Shapley Additive Explanations).

The proposed system follows a multi-layered architecture consisting of:

1. Data Layer – collects attendance, behavioral, and academic information.
2. Processing Layer – cleans data and performs feature engineering.
3. AI Layer – applies Random Forest, XGBoost, and LSTM models.
4. Explainability Layer – uses SHAP to identify the importance of different factors.
5. Decision Layer – generates risk classifications and intervention recommendations.

The methodology uses a dataset of 320 students and 38,400 records, with features including attendance percentage, absence streaks, engagement scores, and academic marks. A dynamic risk score is calculated using weighted contributions from attendance, engagement, and academic performance. Students are classified into Low Risk, Medium Risk, or High Risk categories.

Results show that the hybrid model outperforms individual machine learning models:

• Random Forest: 91% accuracy
• XGBoost: 93% accuracy
• LSTM: 92% accuracy
• Proposed Hybrid Model: 94% accuracy

The system successfully identifies high-risk students early, with a low false-negative rate. Findings reveal that attendance is the strongest predictor of dropout risk, followed by engagement and academic performance. Students with attendance below 65% are significantly more likely to drop out.

The study concludes that integrating behavioral analytics, machine learning, and Explainable AI creates a reliable and scalable solution for early dropout prediction. The system can help educational institutions take proactive measures to improve student retention and academic success. However, challenges remain regarding data quality, privacy concerns, and the system’s ability to generalize across different institutions.

Conclusion

The proposed Early Warning System effectively identifies at-risk students and enables timely intervention. The proposed system provides a robust and scalable solution for early identification of at-risk students, contributing significantly to the field of intelligent educational systems and data-driven decision-making. The proposed system demonstrates significant potential for deployment in real-world smart educational environments and contributes to the advancement of AI-driven academic decision support systems.

References

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Copyright

Copyright © 2026 Arvind Bagale, Dr. Vijay Singh Rathore. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.