Cohesion network analysis: Predicting course grades and generating sociograms for a romanian moodle course

Online collaborative learning environments open new research opportunities, for example, the analysis of learning outcomes, the identification of learning patterns, the prediction of students’ behaviors, and the modeling and visualization of social relations and trends among students. Moodle is an online educational platform which supports both students and teachers, and can be effectively employed to encourage collaborative learning. Moodle is often used to make inquiries on student homework, exams, to request clarifications, and to make announcements. Our goal is to predict student success based on Cohesion Network Analysis (CNA) and to identify interaction patterns between students (n = 71 who had a sufficient level of participation on the forum) and 4 tutors together with 19 teaching assistants in a Romanian Moodle course. CNA visualizations consider a hierarchical clustering that classifies members into central, active, and peripheral groups. Weekly snapshots are generated to better understand students’ evolution throughout the course, while correlating their activities with specific course events (e.g., homework deadlines, tests, holidays, exam, etc.). Several regression models were trained based on the generated CNA indices and the best model achieves a mean average error below.5 points when predicting partial course grades, prior to the final exam, on a 6-point scale.