Paper Contents
Abstract
AbstractThe increasing complexity and volume of student data in higher education necessitate advanced systems for performance analysis. Traditional methods often fall short in providing timely and actionable insights. This paper presents the development and implementation of a Student Performance Analysis System (SPAS) designed to predict and analyze student outcomes using data mining techniques. The system employs classification algorithms to assess student performance in specific courses, enabling educators to identify at-risk students and intervene proactively. By integrating predictive analytics, SPAS offers a dynamic tool for monitoring academic progress and enhancing educational strategies. The study demonstrates the efficacy of SPAS in providing educators with a comprehensive understanding of student performance, thereby facilitating informed decision-making and targeted support interventions.Introductionthe effective assessment and enhancement of student performance have become paramount. Traditional methods of evaluation often fail to provide timely and actionable insights into student progress. The advent of data mining techniques has paved the way for more sophisticated approaches to analyzing student performance. Similarly, a project developed a Python-based SPAS that generates detailed reports on student performance, including averages, grade distributions, and comparisons across subjects. This system aims to provide educators and administrators with comprehensive insights into student achievements, facilitating data-driven decision-making .These examples underscore the potential of SPAS in transforming educational practices by providing a data-driven approach to student performance analysis. By integrating such systems, educational institutions can foster an environment of continuous improvement and personalized learning.
Copyright
Copyright © 2025 PAYAL. This is an open access article distributed under the Creative Commons Attribution License.
