Authors:
J. Angelin Jeba, S. Rubin Bose, Vanessa M. Willemse, Ennouni Assia, Sureshkumar Somayajula, A. Shyam Sundhar

Addresses:
Departement of Electronics and Communication Engineering, S.A. Engineering College, Chennai, Tamil Nadu, India. School of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. Department of Education, Northern Cape Department of Education, Kimberley, Northern Cape, South Africa. Khenifra Higher School of Technology, Sultan Moulay Slimane University, Béni-Mellal, Béni Mellal-Khénifra, Morocco. Departement of Computer Science and Technology, Sunlife Canada Financials, Toronto, Ontario, Canada. Department of Information and communication, Tampere University, Tampere, Pirkanmaa, Finland.

Abstract:

Predicting student academic performance has become a concrete challenge for higher education institutions trying to keep students from slipping through the cracks. The earlier you catch a struggling student, the more you can do about it—yet most traditional assessment systems flag problems only after the damage is done. This paper presents the AI Student Performance Predictor. It is a machine learning system that classifies student performance into three categories: High, Medium, and Low. The system runs a Random Forest Classification algorithm trained on a synthetic dataset of 250 student records built around five features: Cumulative Grade Point Average across three semesters, internal exam marks, class attendance, daily self-study hours, and weekly practice questions. A weighted scoring method combines these features to reflect their relationship to academic outcomes. The Random Forest model achieved 82.0% classification accuracy on the test set, outperforming five other classifiers, including Decision Trees (74.0%), Support Vector Machines (76.0%), and Logistic Regression (70.0%). Feature importance analysis showed CGPA as the strongest single predictor (31.2%), followed by internal marks (24.8%), attendance (19.7%), study hours (14.3%), and practice questions (10.0%).

Keywords: Machine Learning; Random Forest Classifier; Student Performance Prediction; Educational Data Mining; Academic Analytics; Feature Importance; Classification Algorithm; Early Warning System; Higher Education Technology.

Received on: 12/03/2025, Revised on: 13/05/2025, Accepted on: 10/07/2025, Published on: 07/09/2026

DOI: 10.69888/FTSTL.2026.000779

FMDB Transactions on Sustainable Techno Learning, 2026 Vol. 4 No. 3, Pages: 99-109

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