A Comprehensive Machine Learning Framework for Students Performance Prediction and Early Academic Risk Identification

Abstract

The rise of digital learning systems has produced massive amounts of data related to students. Using this data to make predictions via analytics may be used to assist with early intervention and improve student outcomes. This paper introduces a conceptual idea on developing a machine-learning framework to help predict student performance using academic, demographic, and behavioral characteristics. The proposed framework will provide guidelines/requirements for conducting data preprocessing, extracting features, and applying supervised learning techniques. The framework will also provide an opportunity for researchers to use ensemble learning methods to create more accurate and reliable predictions. This research contributes to educational data mining by providing a systematic, theory-driven perspective on predicting academic performance and providing institutional support for decision making.

Introduction

The text describes a machine learning–based framework for predicting student academic performance using educational data. It highlights that traditional evaluation methods, which rely mainly on final exams, do not capture the full picture of a student’s learning journey. With the growth of digital education systems, large amounts of student data (attendance, grades, participation, study habits, etc.) are now available, making it possible to use machine learning for more accurate and continuous performance analysis.

The study proposes a predictive system that uses multiple machine learning models to analyze academic, behavioral, and demographic factors together. Its goals include predicting student outcomes, identifying key factors influencing performance, supporting early intervention for at-risk students, and comparing different ML algorithms for accuracy and reliability.

Existing research shows the use of methods like decision trees, SVM, random forests, gradient boosting, and deep learning. While these models can achieve good accuracy, challenges remain such as poor interpretability, data imbalance, overfitting, and limited real-world adaptability.

The research gap identified is that most studies focus only on accuracy rather than practical application, often analyzing features separately and producing models that are difficult for educators to interpret. Many models also lack generalization across institutions.

The proposed methodology includes data collection from academic records, data cleaning, feature engineering (such as attendance, study time, participation, and marks), training multiple ML models, and evaluating their performance to identify the best approach.

Conclusion

This paper discusses the use of machine learning methods to forecast the performance of students through the analysis of academic and behavioural data. The results demonstrate that by combining several predictive models together, it is possible to increase the accuracy of predictions. The framework presented in this paper was created to be simple enough for real world applications but still provide insight into who requires additional help. The results of this study indicate that while the methods for prediction are showing promise, there remains a considerable opportunity for improvements to be made with respect to the quality of the datasets and the types of features used for predictions. Further studies will need to address these issues. In general, this research provided evidence that machine learning provides new possibilities for enhancing the academic decision-making process.

References

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Copyright

Copyright © 2026 Soniya Patil, Nirmala Shinge . 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.