Student Behavior Patterns in a Virtual Learning Environment

This work focuses on the identification of student behavior patterns obtained from their interactions on a virtual learning Environment (VLE). Clustering techniques were used to classify certain indicators and to obta in groups of students with similar characteristics. The activ ities performed are directly related to four Comput er Science degree courses in the Distance Education modality. Generally, our results show that students interacte d more with online forum, followed by the quiz, tasks, instant messaging, resources, and twitter. The knowledge ac quired via the data mining techniques helped to discover certa in characteristics of their online interaction, whi ch should be taken into account when enhancing the teaching-lear ning process.