Students' LMS interaction patterns and their relationship with achievement: A case study in higher education

The use of Learning Management Systems has grown exponentially in the last several years and has come to have a strong effect on the teaching-learning process, particularly in higher education. The present study intends to examine students' asynchronous learning processes via an Educational Data Mining approach using data extracted from the Moodle logs of students who were grouped according to similar behaviors regarding effort, time spent working, and procrastination. The behaviors were then matched with different levels of achievement. First, the different patterns of students' involvement in the learning process in a Learning Management System were clustered. Second, the different variables selected from the Moodle records were studied to see if they were equally suitable for the configuration of student clusters. Third, the relationships between those patterns to students' final marks were examined. After analyzing the log data gathered from a Moodie 2.0 course in which 140 undergraduate students were enrolled, four different patterns of learning with different final marks were found. Additional results showed that there are variables more related to achievement and more suitable to group the students on the basis of which the different groups were characterized, namely, two Task Oriented Groups (socially or individually focused) and two Non Task Oriented Groups (procrastinators or non-procrastinators). These results have implications in the design of interventions for improving students' learning processes and achievement in LMSs. (C) 2016 Elsevier Ltd. All rights reserved.