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A Comparison of Two Different Methods to Individualize Students and Skills

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Artificial Intelligence in Education (AIED 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7926))

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Abstract

One of the most popular methods for modeling students’ knowledge is Corbett and Anderson’s [1] Bayesian Knowledge Tracing (KT) model. The original Knowledge Tracing model does not allow for individualization. In this work, we focus on comparing two different individualized models: the Student Skill model and the two-phase model, to find out which is the best for formulating the individualization problem within a Bayesian networks framework.

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References

  1. Corbett, A.T., Anderson, J.R.: Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling and User-Adapted Interaction 4, 253–278 (1995)

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  2. Pardos, Z.A., Heffernan, N.T.: Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing. In: Proceedings of the 18th International Conference on User Modeling, Adaptation and Personalization, pp. 225–266 (2010)

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  3. Pardos, Z.A., Heffernan, N.T.: Using HMMs and bagged decision trees to leverage rich features of user and skill from an intelligent tutoring system dataset. Journal of Machine Learning Research W & CP (in press)

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  4. Wang, Y., Heffernan, N.T.: The Student Skill Model. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 399–404. Springer, Heidelberg (2012)

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Author information

Authors and Affiliations

  1. Worcester Polytechnic Institute, USA

    Yutao Wang & Neil Heffernan

Authors
  1. Yutao Wang
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  2. Neil Heffernan
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Editor information

Editors and Affiliations

  1. Institute for Creative Technologies, University of Southern California, 12015 Waterfront Dr., 90094, Playa Vista, CA, USA

    H. Chad Lane

  2. School of Information Technologies, University of Sydney, 2006, Sydney, NSW, Australia

    Kalina Yacef

  3. School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA

    Jack Mostow

  4. Department of Psychology, University of Memphis, 38152, Memphis, TN, USA

    Philip Pavlik

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© 2013 Springer-Verlag Berlin Heidelberg

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Wang, Y., Heffernan, N. (2013). A Comparison of Two Different Methods to Individualize Students and Skills. 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_125

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  • DOI: https://doi.org/10.1007/978-3-642-39112-5_125

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39111-8

  • Online ISBN: 978-3-642-39112-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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