Inferring Students' Sense of Community from Their Communication Behavior in Online Courses

Abstract

Sense of community is regarded as the reflection of students' feelings of connectedness with community members and commonality of learning expectations and goals. In online courses, sense of community has been proven to influence students' learning engagement and academic performance. Low sense of community is also one of the reasons for drop out. However, existing studies mainly acquire students' sense of community via questionnaires, which demand user efforts and have difficulty in obtaining real-time feeling during students' learning process. In addition, although communication is helpful to enhance students' sense of community, little work has empirically compared the impact of different online communication tools. In this paper, we are motivated to derive students' sense of community from their communication behavior in online courses. Concretely, we first identify a set of features that are significantly correlated with students' sense of community, which not only include their activities carried out in both synchronous and asynchronous online learning environment, but also their linguistic content in conversational texts. We then develop inference model to unify these features for determining students' sense of community, and find that LASSO performs the best in terms of inference accuracy.