A semantic network model for measuring engagement and performance in online learning platforms

Abstract

Due to the increasing global availability of the internet, online learning platforms such as Massive Open Online Courses (MOOCs), have become a new paradigm for distance learning in engineering education. While interactions between instructors and students are readily observable in a physical classroom environment, monitoring student engagement is challenging in MOOCs. Monitoring student engagement and measuring its impact on student performance are important for MOOC instructors, who are focused on improving the quality of their courses. The authors of this work present a semantic network model for measuring the different word associations between instructors and students in order to measure student engagement in MOOCs. Correlation analysis is then performed for identifying how student engagement in MOOCs affect student performance. Real-world MOOC transcripts and MOOC discussion forum data are used to evaluate the effectiveness of this research.