Identifying unresolved issues in online student discussions: A multi-phase dialogue classification approach

Abstract

Automatic tools for analyzing student online discussions are highly desirable for providing better assistance and encouraging participation. This paper presents an approach for automatically identifying student discussions with unresolved issues or unanswered questions. We apply a two-phase classification algorithm. First, we classify “speech acts” of individual messages to identify the roles that the messages play, such as question, answer, issue raising, or acknowledgement. We then use the resulting speech acts as features for identifying discussion threads with unresolved issues or questions. We performed a preliminary analysis of the classifiers and achieved an average accuracy of 78%.