Predicting Student Performance: A Comparative Analysis of Machine Learning Algorithms on HSLC Exam Data
TL;DR
This paper compares three machine learning algorithms—k-Nearest Neighbours, Random Forest, as well as Support Vector Machine—for predicting high school student performance, using actual exam data from 12,211 students in Jorhat, Assam to demonstrate the most accurate predictors of student success.
Abstract
This paper compares three machine learning algorithms—k-Nearest Neighbours (k-NN), Random Forest, as well as Support Vector Machine (SVM)—for predicting high school student performance, using actual exam data from 12,211 students in Jorhat, Assam. The most important decision was to keep all student records (109 absent students and 2 withheld results) instead of deleting them. This methodology made the models more realistic and applicable to real schools. The results demonstrate that Random Forest achieved the highest accuracy at 69.63%, followed by k-NN at 58.08%, and SVM at 57.35%. The total percentage of the grand total and minimum subject mark emerged as the most accurate predictors of student success. Current research demonstrates that it is now possible to identify struggling students with high accuracy, enabling timely interventions that support academic success.