Abstract—Nowadays, Information and Communication Technology (ICT) provides an opportunity to discover new knowledge and create a desirable learning environment. That is why the influence of ICT on education is irrefutable. Technology has changed the learning styles: the way people prefer to learn and improve the quality of their learning. Physical and online classes can be held concurrently so that lecturers and students can interact via learning management systems. A Learning Management System (LMS) is an application software that plays a significant role in educational technology. Such software can be designed to augment and facilitate instructional activities including registration and management of education courses, analyzing skill gaps, reporting, and delivery of electronic courses concurrently. Since all information and corresponding data are recorded and monitored in the LMS, it can provide an accurate insight into student’s online behavior. In general, measuring student performance is an important part of the education system. The fields of learning analytics and educational data mining both emphasize the analysis of educational data in order to improve teaching and learning styles as well as to predict student performance. In the current study, we use data from the Moodle LMS from a collection of courses from a single institution to identify weak/strong students during the course. The result has to be interpretable and understandable as the aim is to give this information to lecturers, who may use the information to improve their course and identify students who may need special attention.
Index Terms—Data mining, clustering, decision tree, rule-based, student.
Parisa Shayan is with School of Humanities and Digital Sciences, Tilburg University, Netherlands (e-mail: P.Shayan@uvt.nl).
Menno van Zaanen is with School of Humanities and Digital Sciences, Tilburg University, Netherlands (e-mail: M.M.vanZaanen@uvt.nl).
Cite: Parisa Shayan and Menno van Zaanen, "Predicting Student Performance from Their Behavior in Learning Management Systems," International Journal of Information and Education Technology vol. 9, no. 5, pp. 337-341, 2019.