A MACHINE LEARNING APPROACH FOR PREDICTING STUDENT PERFORMANCE

Abstract

Machine learning models have gained significant importance in the education sector for predicting student performance. By analysing historical academic records, attendance, behavioural patterns, and other relevant factors, these models can provide insights into students' future performance. Traditional methods of assessing student success often rely on periodic examinations, which may not always capture underlying learning patterns. Machine learning techniques such as regression analysis, decision trees, support vector machines, and deep learning algorithms can effectively identify key performance indicators. These models assist educators in understanding students’ strengths and weaknesses, enabling timely intervention and personalized learning strategies to improve outcomes. The application of machine learning in student performance prediction not only helps academic institutions but also benefits students by offering tailored guidance and support. Predictive models can identify at-risk students, allowing educators to take proactive measures to address learning gaps. Additionally, these models facilitate the development of adaptive learning systems that modify educational content based on individual progress. With the continuous advancement of machine learning techniques, the accuracy of these predictive models continues to improve, making them an essential tool for modern education. The integration of such technologies enhances decisionmaking processes, leading to a more data-driven and student-centric approach to learning.