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Using Graphical Models to Classify Dialogue Transition in Online Q&A Discussions

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Artificial Intelligence in Education (AIED 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6738))

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Abstract

In this paper, we examine whether it is possible to automatically classify patterns of interactions using a state transition model and identify successful versus unsuccessful student Q&A discussions. For state classification, we apply Conditional Random Field and Hidden Markov Models to capture transitions among the states. The initial results indicate that such models are useful for modeling some of the student dialogue states. We also show the results of classifying threads as successful/unsuccessful using the state information.

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References

  1. Kim, J., Chem, G., Feng, D., Shaw, E., Hovy, E.: Mining and assessing discussions on the web through speech act analysis. Proceedings of the Workshop on Web Content Mining with Human Language Technologies at the 5th International Semantic Web Conference (2006)

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  2. Kang, J.H., Kim, J., Shaw, E.: Modeling Successful versus Unsuccessful Threaded Discussions. In: Workshop on Opportunities for Intelligent and Adaptive Behavior in Collaborative Learning Systems, vol. 13 (2010)

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  3. Kang, J., Kim, J.: Profiling Message Roles in Threaded Discussions using an Influence Network Model, internal project report (2010)

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  4. Drummond, J., Kim, J.: Role of Elaborated Answers on Degrees of Student Participation in an Online Question-Answer. American Educational Research Association (2011)

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Author information

Authors and Affiliations

  1. University of Southern California Information Sciences Institute, USA

    Soo Won Seo, Jeon-Hyung Kang & Jihie Kim

  2. University of Pittsburgh, USA

    Joanna Drummond

Authors
  1. Soo Won Seo
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  2. Jeon-Hyung Kang
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  3. Joanna Drummond
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  4. Jihie Kim
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Editor information

Editors and Affiliations

  1. EECS Department/ISIS, Vanderbilt University, TN 37235, Nashville, USA

    Gautam Biswas

  2. Electronic, Electrical and Computer Engineering, University of Birmingham, U.K.

    Susan Bull

  3. School of Information Technologies, University of Sydney, 1 Cleveland Street, 2006, Sydney, Australia

    Judy Kay

  4. College of Engineering, Department of Computer Science and Software Engineering, University of Canterbury, Private Bag 4800, 8140, Christchurch, New Zealand

    Antonija Mitrovic

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© 2011 Springer-Verlag Berlin Heidelberg

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Seo, S.W., Kang, JH., Drummond, J., Kim, J. (2011). Using Graphical Models to Classify Dialogue Transition in Online Q&A Discussions. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds) Artificial Intelligence in Education. AIED 2011. Lecture Notes in Computer Science(), vol 6738. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21869-9_98

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  • DOI: https://doi.org/10.1007/978-3-642-21869-9_98

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21868-2

  • Online ISBN: 978-3-642-21869-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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