International Council for Education, Research and Training

Classifying Student Performance: An In-Depth Analysis Using Machine Learning Algorithms

 C, Suhasini1

1Research Scholar, Statistician, JSS AHER

 B, Madhu2 

2Professor & Deputy Dean (Research), JSS AHER

Abstract

 In recent years, analyzing and predicting student performance has been a significant challenge for educational institutions. Early analysis of student data can reveal their strengths and weaknesses, aiding in the improvement of examination outcomes. Machine learning offers a powerful tool for predicting student performance by utilizing demographic and academic data. We can forecast the results and determine which classification algorithm has the highest accuracy from algorithms such as support vector machines, J48 decision trees, naive bayes, and simple logistic regression. As a result, the accuracy level is addressed in the conclusion of data statistics, featuring classification models. By analyzing the classification algorithms, the logistic regression approach provides a more efficient way for educators to identify patterns and trends in the performance of students. This study enables targeted processes for students who would normally perform poorly on examinations.

Keywords: machine learning, student performance, algorithm, examination

Impact statement

Title: “Classifying Student Performance: An In-Depth Analysis Using Machine Learning Algorithms”:

This study compares various algorithms like Support Vector Machine, J48 Decision Tree, Naive Bayes, and Logistic Regression to predict student performance. It helps educational institutions improve examination outcomes by identifying students’ strengths and weaknesses early on.  The study highlights the effectiveness of machine learning in predicting student performance, aiding educators in identifying patterns and providing targeted support to improve educational outcomes.

The comprehensive student data, including behavioural and psychological factors, to improve prediction accuracy using Real-Time Analysis to Implement real-time data analysis systems to provide immediate feedback and support to students and also formulate educational policies and intervention strategies based on predictive analytics to support at-risk students more effectively.

The Digital Transformation Integrating digital tools and platforms to facilitate online learning, virtual labs, and digital libraries and creating innovation hubs within institutions. The Student Support Services is providing the comprehensive support services, including mental health resources, career counselling, and academic advising to help the student for improving their academic performance. 

About The Author

Suhasini C is currently employed as a Statistician at the Department of Bureau of Quality & Statistics, JSS Academy of Higher Education & Research. She completed both her undergraduate (B.Sc.) and postgraduate (M.Sc. in Statistics) degrees at the University of Mysore. With a decade of experience in teaching, Suhasini has served as a Lecturer, specializing statistics in time series analysis and data mining techniques. She has taught Statistics to both MSc and undergraduate students across various colleges in Mysore, Karnataka. Suhasini is also pursuing a Ph.D. in the Faculty of Life Sciences at JSS Academy of Higher Education and Research, Mysuru.

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