Estimating Academic Student Performance: Analyzing Application of Data Mining Techniques in Distance Learning

Abstract

Educational environments have incorporated the use of software to support learning activities. A Learning Management System (LMS) is essential to dispatch distance learning courses to students. A LMS typically stores large volumes of data, recording in detail the activities performed by students. These data can be used to discover relevant information that can help teachers in the management of the teaching-learning process. In this study, by using data mining techniques, we investigate how to obtain inferences about the performance of students in distance learning courses based on data obtained from a Learning Management System. Some experiments conducted in this research indicate the viability of this proposal, achieving accuracy rates between 73% and 80% in estimates students’ academic performance.