Analysing online education-based asynchronous communication tools to detect students' roles

This paper studies the application of Educational Data Mining to examine the online communication behaviour of students working together on the same project in order to identify the different roles played by the students. Analysis was carried out using real data from students’ participation in project communication tools. Several sets of features including individual attributes and information about the interactions between the project members were used to train different classification algorithms. The results show that considering the individual attributes of students provided regular classification performance. The inclusion of information about the reply relationships among the project members generally improved mapping students to their roles. However, “time-based” features were necessary to achieve the best classification results, which showed both precision and recall of over 95% for a number of algorithms. Most of these “time-based” features coincided with the first weeks of the experience, which indicates the importance of initial interactions between project members