Revealing latent traits in the social behavior of distance learning students.

This paper proposes a multilayered methodology for analyzing distance learning students' data to gain insight into the learning progress of the student subjects both in an individual basis and as members of a learning community during the course taking process. The communication aspect is of high importance in educational research. Additionally, it is difficult to assess as it involves multiple relationships and different levels of interaction. Social network analysis (SNA) allows the visualization of this complexity and provides quantified measures for evaluation. Thus, initially, SNA techniques were applied to create one-mode, undirected networks and capture important metrics originating from students' interactions in the fora of the courses offered in the context of distance learning programs. Principal component analysis and clustering were used next to reveal latent students' traits and common patterns in their social interactions with other students and their learning behavior. We selected two different courses to test this methodology and to highlight convergent and divergent features between them. Three major factors that explain over 70% of the variance were identified and four groups of students were found, characterized by common elements in students' learning profile. The results highlight the importance of academic performance, social behavior and online participation as the main criteria for clustering that could be helpful for tutors in distance learning to closely monitor the learning process and promptly interevent when needed.