Enhancing Academic Performance through Machine Learning: A Comprehensive Study of Student Academic Tracking Systems
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116-133
Digital Object Identifier:
10.58578/kijeit.v2i3.7541
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Authors:
Samuel Olofu Owoicho1 
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Authors retain copyright and grant the journal right of first publication.
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Abstract
The rapid advancement of technology has created new opportunities to enhance education, with machine learning (ML) emerging as a transformative tool. This study presents the development and evaluation of a comprehensive academic tracking system designed to monitor and categorize students based on performance metrics, while also providing functionality beyond simple grade reporting. Unlike traditional systems that serve primarily as repositories for academic scores, the proposed system offers integrated tools for tracking attendance, monitoring academic progress, managing assignments, and generating early alerts for at-risk students. Developed using Python for backend logic, React for frontend implementation, and MySQL for secure data management, the web-based platform was designed to improve real-time access and usability for both students and educators. The system incorporates a multifaceted methodology to analyze a wide range of student-related factors, including demographic data (e.g., age, gender, socioeconomic background), academic performance (e.g., grades, attendance), and behavioral indicators (e.g., participation and assignment submissions). The model classifies students into low, average, and high-performing groups using machine learning techniques, enabling more targeted interventions. When tested with real academic data from tertiary institutions in Nigeria, the proposed system demonstrated superior accuracy and efficiency in tracking and predicting student performance compared to existing solutions. These findings underscore the system’s potential to support data-driven decision-making in educational environments and to enhance learning outcomes through early identification and personalized support strategies.
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