Social network analysis techniques and text mining to assess the learning process of students' participation in the online discussion

Abstract

On a daily basis, a large amount of data is gathered through the participation of students in elearning environments. This wealth of data is an invaluable asset to researchers as they can utilize it in order to generate conclusions and identify hidden patterns and trends by using big data analytics techniques. The purpose of this study is a threefold analysis of the data that are related to the participation of students in the online forums of their University. In one hand the content of the messages posted in these fora can be efficiently analyzed by text mining techniques. On the other hand, the network of students interacting through a forum can be adequately processed through social network analysis techniques. Still, the combined knowledge attained from both of the aforementioned techniques, can provide educators with practical and valuable information for the evaluation of the learning process, especially in a distance learning environment. In addition, we propose a classification via decision tree approach in order to forecast students' learning performance based on forum data. The study was conducted by using real data originating from the online forums of the Hellenic Open University (HOU). The analysis of the data has been accomplished by using the R tools, in order to analyze the structure and the content of the exchanged messages in these fora as well as to model the interaction of the students in the discussion threads.