Abstract
Online discussion board has become increasingly popular in higher education. As a step towards analyzing the role that students and instructors play during the discussion process and assessing students’ learning from discussions, we model different types of contributions made by instructors and students with a dialogue-state model. By analyzing frequent Q&A discussion patterns, we have developed a graphic model of dialogue states that captures the information role that each message plays, and used the model in analyzing student discussions, presenting several viable ap-proaches including CRF, SVM, and decision tree for the state classification. Such analyses can give us a new insight on how students interact in online discussions and kind of assistance needed by the students.
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References
Boyer, K.E., Ha, E.Y., Wallis, M.D., Phillips, R., Vouk, M.A., Lester, J.C.: Discovering tutorial dialogue strategies with hidden Markov models. In: Proc. AIED (2009)
Fossati, D., Di Eugenio, B., Ohlsson, S., Brown, C., Chen, L.: Generating proactive feedback to help students stay on track. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010, Part II. LNCS, vol. 6095, pp. 315–317. Springer, Heidelberg (2010)
Kim, J., et al.: Mining and assessing discussions on the web through speech act analysis. In: Workshop on Web Content Mining with Human Language Technologies at ISWC (2006)
McCallum, A.: MALLET: a machine learning for language toolkit (2002), http://mallet.cs.umass.edu
McLaren, B., et al.: Using Machine Learning Techniques to Analyze and Support Mediation of Student E-Discussions. In: Proceedings of AIED (2007)
Mu, J., Stegmann, K., Mayfield, E., Rose, C., Fischer, F.: The ACODEA framework: Developing Segmentation and Classification Schemes for Fully Automatic Analysis of Online Discussions. In: Proc. CSCL (2012)
Ravi, S., Kim, J.: Profiling Student Interactions in Threaded Discussions with Speech Act Classifiers. In: Proceeding of AIED (2007)
Seo, S.W., Kang, J.-H., Drummond, J., Kim, J.: Using Graphical Models to Classify Dialogue Transition in Online Q&A Discussions. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds.) AIED 2011. LNCS (LNAI), vol. 6738, pp. 550–553. Springer, Heidelberg (2011)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. (2005)
Yoo, J., Kim, J.: Predicting Learner’s Project Performance with Dialogue Features in Online Q&A Discussions. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 570–575. Springer, Heidelberg (2012)
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Shen, S., Kim, J. (2013). Modeling the Process of Online Q&A Discussions Using a Dialogue State Model. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds) Artificial Intelligence in Education. AIED 2013. Lecture Notes in Computer Science(), vol 7926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_86
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DOI: https://doi.org/10.1007/978-3-642-39112-5_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39111-8
Online ISBN: 978-3-642-39112-5
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