Higher Education Student's Performance Evaluation Using Machine Learning Techniques

Abstract

Failure and success in the classroom have real-world implications for achieving economic success in the knowledge-based economy. Using early detection markers (such as age, reading frequency, and CGPA), this research aims to forecast the likelihood of students' academic performance in order to provide prompt and effective remediation. On the basis of secondary data acquired from students' information systems, a machine learning approach was employed to create a model. In this paper, our main aim is to predict student performance for 3 specific factors student scientific book reading frequency, extra work conditions, and weekly study time. So we are using five machine learning algorithms KNN, Random forest, Decision tree, Linear regression, and GBC, and also use almost 1200 student attribute datasets. For students with extra work conditions random forest algorithms given the highest 99 % accuracy. For student scientific book reading frequency random forest and decision tree are given the highest 98 % accuracy. For students weekly study hours random forest and KNN given highest 97 % accuracy.