Multi-attribute categorization of MOOC forum posts and applications to conversational agents

Discussion forums are among the most common interaction tools offered by MOOCs. Nevertheless, due to the high number of students enrolled and the relatively small number of tutors, it is virtually impossible for instructors to effectively monitor and moderate them. For this reason, teacher-guided instructional scaffolding activities may be very limited, even impossible in such environments. On the other hand, students who seek to clarify concepts may not get the attention they need, and lack of responsiveness often favors abandonment. In order to mitigate these issues, we propose in this work a multi-attribute text categorization tool able to automatically detect useful information from MOOC forum posts including intents, topics covered, sentiment polarity, level of confusion and urgency. Extracted information may be used directly by instructors for moderating and planning their interventions as well as input for conversational software agents able to engage learners in guided, constructive discussions through natural language. The results of an experiment aimed at evaluating the performance of the proposed approach on an existing dataset are also presented, as well as the description of an application scenario that exploits the extracted information within a conversation agents’ framework.