Mining Collaborative Patterns in Tutorial Dialogues
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Published
Dec 1, 2010
Sidney D'Mello
Andrew Olney
Natalie Person
Abstract
We present a method to automatically detect collaborative patterns of student and tutor dialogue moves. The method identifies significant two-step excitatory transitions between dialogue moves, integrates the transitions into a directed graph representation, and generates and tests data-driven hypotheses from the directed graph. The method was applied to a large corpus of student-tutor dialogue moves from expert tutoring sessions. An examination of the subset of the corpus consisting of tutor lectures revealed collaborative patterns consistent with information-transmission, information-elicitation, off topic-conversation, and student initiated questions. Sequences of dialogue moves within each of these patterns were also identified. Comparisons of the method to other approaches and applications towards the computational modeling of expert human tutors are discussed.
How to Cite
D’Mello, S., Olney, A., & Person, N. (2010). Mining Collaborative Patterns in Tutorial Dialogues. Journal of Educational Data Mining, 2(1), 2–37. https://doi.org/10.5281/zenodo.3554737
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Keywords
tutorial dialogue, pattern mining, expert tutors, lecutres, likelihood metric, information transmission, information elicitation, human tutoring, educational data mining
References
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COHEN, P., KULIK, J. AND KULIK, C. 1982. Educational outcomes of tutoring: A meta-analysis of findings. American Educational Research Journal 19, 237-248.
COOKE, N.J., NEVILLE, K.J. AND ROWE, A.L. 1996. Procedural network representations of sequential data. Human-Computer Interaction 11, 29-68.
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CORBETT, A., ANDERSON, J., GRAESSER, A., KOEDINGER, K. AND VANLEHN, K. 1999. Third generation computer tutors: Learn from or ignore human tutors? In Proceedings of CHI Conference on Human Factors in Computing Systems ACM, New York, 85 - 86.
CORE, M., MOORE, J. AND ZINN, C. 2003. The role of initiative in tutorial dialogue. In 10th Conference of the European Chapter of the Association for Computational Linguistics Linguistic Data Consortium, Budapest, Hungary.
CROMLEY, J. AND AZEVEDO, R. 2005. What Do Reading Tutors Do?: A Naturalistic Study of More and Less Experienced Tutors in Reading. Discourse Processes 40, 83-113.
D'MELLO, S., HAYS, P., WILLIAMS, C., CADE, W., BROWN, J. AND OLNEY, A. in press. Collaborative Lecturing by Human and Computer Tutors In Proceedings of 10th International Conference on Intelligent Tutoring Systems, J. KAY AND V. ALEVEN Eds. Springer, Berlin / Heidelberg.
D'MELLO, S., TAYLOR, R. AND GRAESSER, A. 2007. Monitoring affective trajectories during complex learning. In Proceedings of the 29th Annual Cognitive Science Society D. MCNAMARA AND G. TRAFTON Eds. Cognitive Science Society, Austin, TX, 203-208. DEL SOLDATO, T. AND DU BOULAY, B. 1995. Implementation of motivational tactics in tutoring systems. International Journal of Intelligence in Education 6, 337- 378.
DERRY, S. AND POTTS, M. 1998. How tutors model students: A study of personal constructs in adaptive tutoring. American Educational Research Journal 35, 65-99.
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