Educational Data Mining for Early Detection of At-Risk Students

Abstract

This project aims to utilize sophisticated data mining methods to recognize students who may be at risk of academic underperformance or leaving school, allowing educational institutions to intervene early. The project\'s goal is to create a predictive model for early risk identification by examining extensive datasets that include grades, attendance, behavior patterns, and demographic details. This system will be incorporated into current educational platforms, providing educators and institutions with a user-friendly dashboard to track at-risk students and offer timely, targeted assistance. The project requires access to high-quality data, adherence to privacy laws such as FERPA and GDPR, and collaboration with education professionals to gain meaningful insights. Key technologies involved include Python for backend processing, frontend frameworks like React or Angular, and Python-based libraries for data analysis. The project will progress through several stages: requirements analysis, data gathering and preprocessing, feature engineering, model development, deployment, and pilot testing. By enabling educators to proactively support vulnerable students, this system aims to enhance student retention and academic achievement.

Introduction

Academic failure and student dropouts remain pressing issues, often identified too late by traditional exam-based systems. The abundance of student performance and behavioral data on digital platforms presents an opportunity for predictive intervention using Educational Data Mining (EDM).

The proposed EduRisk AI framework uses a Random Forest classifier to identify at-risk students early by analyzing features such as attendance, total score, project performance, study hours, and stress levels. The system integrates seamlessly with institutional LMS and SIS platforms, preprocesses and normalizes data, extracts high-impact features, and delivers real-time predictions through a Flask-based interactive dashboard.

Key contributions include:

• Predictive Modeling: Early identification of students at academic risk using ensemble machine learning.
• Real-Time Monitoring: Automated data collection and analytics integration with LMS/SIS.
• Interactive Dashboard: Visualization of individual risk scores, feature importance, and trend tracking.
• Ethical Compliance: Secure handling of student data with adherence to privacy standards.

Experimental results show high accuracy, precision, recall, and F1-score, with low latency (42 ms per prediction) and real-time throughput (65 predictions/sec). The system enables timely academic interventions, improving retention and personalized support.

Conclusion

This study offers a data-driven framework that uses machine learning (ML) and educational data mining (EDM) techniques to help identify at-risk individuals in higher education early on. With 91.3% predictive accuracy, 0.88 precision, and an F1-score of 0.89, empirical validation outperforms conventional regression and decision tree baselines by 7%. By combining Random Forest, Gradient Boosting (XGBoost), and Support Vector Machines, the suggested ensemble design strikes a compromise between interpretability, computational efficiency, and predictive capability, making it appropriate for widespread academic use. Through the collaborative integration of academic, behavioral, and demographic information into a single prediction model—which is then further operationalized through an interactive visualization dashboard—the research improves the field of educational analytics. This dashboard makes it possible to track student risk profiles in real time, giving teachers the ability to launch prompt, evidence-based interventions that improve overall performance and academic retention. The viability of automated early-warning frameworks in institutional decision support environments is validated by comparative analysis, which shows quantifiable gains over traditional academic performance tracking systems. Because of its versatility and scalability, the framework can be used in a variety of educational institutions with different curriculum and grading systems. The study advances the field of educational analytics by combining academic, behavioral, and demographic data into a single prediction model. This model is then further operationalized through an interactive visualization dashboard. With the use of this dashboard, teachers can monitor student risk profiles in real time and implement timely, research-based interventions that enhance academic retention and overall performance. Comparative analysis demonstrates measurable improvements over conventional academic performance tracking systems, validating the feasibility of automated early-warning frameworks in institutional decision support environments. The framework\'s adaptability and scalability allow it to be utilized in a range of educational settings with various curricula and grading schemes.

References

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Copyright

Copyright © 2026 Ms. R. Tejaswini, Vasala Pragvamsh , Ganji Prasuna Kumari, V. Sai Vishal. 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.