Java-Based System for Personalized Study Tracking and Performance Prediction

The proposed Java-Based System for Personalized Study Tracking and Performance Prediction provides a simple and effective approach for monitoring and analyzing student study activities. The system records study details such as date, subject, duration, and description using a Java Swing-based interface and stores the data in CSV format. The collected study data is analyzed to generate total study duration, average study duration, active study days, subject-wise study patterns, and study consistency. Based on historical study behavior, the system also provides an estimated future performance level. The results show that the proposed system can help students understand their study habits, identify areas requiring additional attention, and support personalized performance analysis.

[1] C. Romero and S. Ventura, “Educational Data Mining: A Review of the State of the Art,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 40, no. 6, pp. 601–618, 2010. [2] G. Siemens and P. Long, “Penetrating the Fog: Analytics in Learning and Education,” EDUCAUSE Review, vol. 46, no. 5, pp. 30–40, 2011. [3] R. S. J. d. Baker and P. S. Inventado, “Educational Data Mining and Learning Analytics,” in Learning Analytics: From Research to Practice, J. A. Larusson and B. White, Eds. New York, NY, USA: Springer, 2014, pp. 61–75. [4] Oracle Corporation, “Java SE Documentation,” Oracle. [Online].https://www.oracle.com/java/technologies/javase-documentation.html. [5] Oracle Corporation, “Using Swing Components,” The Java Tutorials.[Online].  https://docs.oracle.com/javase/tutorial/uiswing/components/. [6] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Waltham, MA, USA: Morgan Kaufmann, 2011. [7] G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning: With Applications in R. New York, NY, USA: Springer, 2013.