On the Development of a Model to Prevent Failures, built from Interactions with Moodle

Alvaro Figueira, Bruno Cabral

Nowadays, students are used to be assessed through an online platform. Generically, educators have stepped up from a period in which they endured the transition from paper to digital. In many courses today, the evaluation methodology also fosters the students’ online participation in forums, the download, and upload modified files, or even the participation in group activities. At the same time, new pedagogy theories that promote the active participation of students in the learning process, and the systematic use of problem-based learning, are being adopted with the aid of an eLearning system.
In this article, we propose an automatic system that informs students of abnormal deviations of a virtual learning path that leads to succeeding in the course. Our motivation is based on the fact that by obtaining this information earlier in the semester, it may provide students and educators an opportunity to resolve an eventual problem regarding the student’s current online actions towards the course. Our goal is to prevent situations that have a significant probability to lead to a poor grade and, eventually, to failing. Our methodology, described in the paper, can be applied to online courses that integrate the use of an online platform that stores user actions in a log file. The system is based on a data mining process on the log files and on a machine learning algorithm that works paired with the Moodle LMS. Our results show that it is possible to predict grade levels by taking these interaction patterns into consideration.