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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 studentsâ 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,  âtime-basedâ 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 âtime-basedâ features coincided with the  first weeks of the experience, which indicates the importance of initial interactions between project members.

Analysing Online Education-based Asynchronous Communication Tools to Detect Students’ Roles