Predicting GPA and academic dismissal in LMS using educational data mining: A case mining

In this paper, we describe an educational data mining (EDM) case study based on the data collected from learning management system (LMS) of e-learning center and electronic education system of Iran University of Science and Technology (IUST). Our main goal is to illustrate the applications of EDM in the domain of e-learning and online courses by implementing a model to predict academic dismissal and also GPA of graduated students. The monitoring and support of freshmen and first year students are considered very significant in many educational institutions. Consequently, if there are some ways to estimate probability of dismissal, drop out and other challenges within the process of the graduation, and also capable tools to predict GPA or even semester by semester grades, the university officials can design and improve more efficient strategies for education systems especially for e-learning ones which include less known and more complicated problems. To achieve the mentioned goal, a common methodology of data mining has been utilized which is called CRISP. Our results show that there can be confident models for predicting educational attributes. Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community.