DROP RISK PREDICTION SYSTEM USING DATA- DRIVEN ANALYTICS
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Student dropout remains a persistent and complex challenge for educational institutions across the globe, affecting academic performance metrics, institutional reputation, and long-term student success. High dropout rates not only result in financial losses for institutions but also negatively impact students’ career prospects and socio-economic mobility. Therefore, the early identification of students who are at risk of discontinuing their studies has become a strategic priority in modern education systems.
This paper presents the design and implementation of a Drop Risk Prediction System, a comprehensive web-based analytical platform developed using PHP for server-side processing, MySQL for structured data management, and JavaScript for dynamic user interaction and visualization. The system leverages data-driven logic to analyze multiple student-related parameters, including academic performance (marks, GPA trends, subject failures), attendance records, assignment submission patterns, and behavioral indicators. By integrating these diverse data sources, the platform generates a holistic assessment of each student’s academic standing.
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S, S. (2026). Drop Risk Prediction System using Data- Driven Analytics. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.291
S, Sreemathi. "Drop Risk Prediction System using Data- Driven Analytics." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.291.
S, Sreemathi. "Drop Risk Prediction System using Data- Driven Analytics." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.291.
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