A Streamlined Approach to Student Stream Prediction Using an Ensemble Machine Learning Model

Abstract

Finding a stream or course after secondary or senior secondary education is a daunting challenge for students and parents, as numerous options are available in various engineering and non-engineering courses. This decision potentially influences a student’s academic success and career. Most frequently, they take courses with the advice of relatives, neighbors, or career counsellors. Online platforms and Learning Management Systems also exist to offer guidance on stream selection. Still, these systems rely on short-term assessments such as tests, quizzes, or interviews, potentially restricting a student's options. Our research employed the Rajan and Rai (RR) student performance prediction model based on a sophisticated Ensemble Machine Learning approach. Our model incorporates a stack of four multiclass classifiers, namely Decision Tree, k-Nearest Neighbor, Naïve Bayes, and One vs. Rest Support Vector Machine classifiers, and demonstrates a remarkable accuracy rate of 80% for predicting the most suitable academic stream for a student in an Institution. To develop this model, we utilized data from five distinct branches of students. We aim to enhance students' academic success so they can complete their degrees with excellent Grades. Exploring our model in the education sector empowers students with the timely facilities they need for a successful and fulfilling educational journey.