Prediction of Students’ Performance in e- Learning Environment using Data Mining/ Machine Learning Techniques * Brijesh Kumar Verma 1 Research Scholar, Amity Institute of Information Technology (AIIT), Amity University, Lucknow, U.P, INDIA vermamtech05@gmail.com Hemant Kumar Singh 2 Associate Professor and Head Department of Computer Science & Engineering SMS Institute of Technology, Lucknow, U.P. INDIA hemantbib@gmail.com Dr. Nidhi Srivastava 3 Assistant Professor, Amity Institute of Information Technology (AIIT), Amity University, Lucknow, U.P, INDIA nsrivastava2@lko.amity.edu Abstract—The COVID-19 pandemic has drastically changed the way od of learning. During this pandemic the learning has shifted from offline to online. student’s performance prediction based on their relevant information has emerged new area for educational institutions for improving teaching learning process, changes in course curriculum. Machine leaning technology can be helpful in predicting the performance of student and accordingly the institutions can make required changes in in their lecture delivery and curriculum. This paper utilized some machine learning methodologies to predict the students’ performance. Educational data of open University(OU) is analysed Based on parameters that are demographic, engagement and performance. In the experimental analysis. In the experimental analysis, the k-NN approach performed best in some cases and ANN performed best in other cases among all compared algorithms on OU dataset. Keywords: E-Learning Environment (ELE), Educational Data Mining(EDM), Machine Learning (ML), Performance Classification. 1. Introduction Substantial use of Internet technology has transformed the education system from traditional offline mode to online/blended mode called as E-Learning Environment (ELE). This has emerged as a new area of research for researchers [1]. Jani et al. [2] reasoned that blended learning of face-to-face and using the ELE platform improved the student’s understanding and performance as well. During the COVID-19 pandemic all the academic institutions are closed and shifted to online mode that increased the importance of E-leaning Environment [3]. The major challenges for the educational institutions is actual and trustworthy evaluation of student’s performance on ELE. It becomes very difficult and complex to e-access the students’ performance without cheating by students from Internet, written notes and any other sources [4]. The real students’ performance prediction will be helpful to teacher’s/course coordinators at the initial phases of the course who needs attention and help [5]. D.O.I - 10.51201/JUSST/21/05179 Journal of University of Shanghai for Science and Technology ISSN: 1007-6735 Volume 23, Issue 5, May - 2021 Page-586 http://doi.org/10.51201/JUSST/21/05179