Fuzzy-Based Early Academic Risk Prediction System in Educational Institutions

Abstract

Improving possession, performance, and overall academic results requires early identification of students at education instability. The ambiguity and complexity included in student performance data may not be well captured by the traditional prediction algorithms, which frequently depend on strict thresholds and binary classifications. In order to represent an imprecise, ambiguous, and multi-dimensional academic indicator, this study proposed a fuzzy-based, early prediction of educational instability among students.This method incorporates significant input variables, including participation level, quiz results, attendance rate, assignment completion, and socio-academic aspects. Using fuzzy theorizing rules, and team effort of the academic experts, these inputs are transformed into linguistic variables and processed through a fuzzy rule based system. The model applies fuzzification, rule evaluation, aggregation, and de - fuzzification to generate a continuous risk score, Classifying students into risk levels such as low risk, medium risk, and high risk. With the help of the suggested system, tutors and administrators can spot students who are at risk early in the semester and provide prompt action like academic support program, counselling, or mentoring. According to experimental evaluation, the fuzzy-based approach improves early detection accuracy while preserving decision rule transparency by offering dependable and flexible predictions. The system provides educational institutions looking to improve student success through proactive academic monitoring with a scalable and adaptable solution.

Introduction

The text focuses on developing a fuzzy logic-based system for early academic risk prediction in students.

It begins by explaining that identifying students at risk of poor academic performance early is important for improving learning outcomes and institutional effectiveness. However, traditional methods—such as using GPA, test scores, and attendance—are limited because they cannot fully capture the complexity of student performance. In addition, many machine learning models, although accurate, are often difficult to interpret, which reduces their usefulness in educational decision-making.

To address these issues, the study proposes using fuzzy logic, which allows for gradual rather than strict classifications and can handle uncertainty and imprecise data. By using linguistic rules and combining different academic and social factors, fuzzy logic can generate a more flexible and understandable risk score for students.

The literature review highlights several related studies showing that fuzzy logic has been successfully used in academic prediction, mental health risk assessment, and hybrid AI systems. These studies demonstrate that fuzzy approaches are better at handling uncertainty compared to traditional models, though many focus on performance prediction rather than early warning systems.

The research problem section points out that current systems rely on rigid cut-off values (like minimum GPA or attendance thresholds), which are not effective for detecting gradual academic decline. To overcome this, the study proposes a fuzzy logic-based early warning system using secondary data from 150 undergraduate students across various commerce-related programs, many of whom face financial and personal challenges affecting their studies.

Conclusion

This study shows how well a fuzzy inference system (FIS) can detect early indicators of academic instability in undergraduate students, especially those who work part-time. The suggested fuzzy-based methodology takes into account uncertainty, imprecision, and gradual changes in student performance, in contrast to conventional evaluation techniques that depend on strict criteria like attendance percentages or CGPA cut-offs. The approach offers a more accurate and adaptable evaluation of student risk levels by including a variety of contributing elements, including working hours, attendance, tiredness levels, study time, and academic performance. The results demonstrate that, despite its financial advantages, part-time work can have a substantial impact on academic stability if it is not well balanced. The fuzzy model successfully categorizes students into different risk levels (low, moderate, high), enabling early identification of vulnerable students. This early detection is crucial for timely intervention by educators and institutional administrators. In summary, the use of fuzzy logic in academic risk prediction presents a viable substitute for traditional monitoring methods. It encourages a more sophisticated, data-driven approach to arranging interventions and evaluating students. Future studies might concentrate on creating real-time decision-support tools for educational institutions, validating the model with bigger and more varied datasets, and combining machine learning methods with fuzzy systems.

References

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Copyright

Copyright © 2026 Dr. Mary Tency E. L., Monicasri L. S., Rithika S , Shaline S, Gayathri B, Harish M. 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.