Mining Collaborative Patterns in Tutorial Dialogues
作者： Natalie Person , Andrew Olney , Sidney D'Mello
关键词: Science education 、 Interpersonal communication 、 Representation (mathematics) 、 Computer science 、 Directed graph 、 TUTOR 、 Discourse analysis 、 Information elicitation 、 Educational data mining 、 Artificial intelligence 、 Natural language processing
摘要: We present a method to automatically detect collaborative patterns of student and tutor dialogue moves. The identifies significant two-step excitatory transitions between moves, integrates the into directed graph representation, generates tests data-driven hypotheses from graph. was applied large corpus student-tutor moves expert tutoring sessions. An examination subset consisting lectures revealed consistent with information-transmission, information-elicitation, off topic-conversation, initiated questions. Sequences within each these were also identified. Comparisons other approaches applications towards computational modeling human tutors are discussed.
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