Discourse learning: an investigation of dialogue act tagging using transformation-based learning
Discourse learning: an investigation of dialogue act tagging using transformation-based learning
Using Machine Learning Techniques to Analyze and Support Mediation of Student E-Discussions
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Profiling Student Interactions in Threaded Discussions with Speech Act Classifiers
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
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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%.