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Detecting the Moment of Learning

  • Conference paper
Intelligent Tutoring Systems (ITS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 6094))

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Abstract

Intelligent tutors have become increasingly accurate at detecting whether a student knows a skill at a given time. However, these models do not tell us exactly at which point the skill was learned. In this paper, we present a machine-learned model that can assess the probability that a student learned a skill at a specific problem step (instead of at the next or previous problem step). Implications for knowledge tracing and potential uses in “discovery with models” educational data mining analyses are discussed, including analysis of which skills are learned gradually, and which are learned in “eureka” moments.

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Author information

Authors and Affiliations

  1. Department of Social Science and Policy Studies, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, 01609, USA

    Ryan S. J. d. Baker

  2. Department of Computer Science, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA, 01609, USA

    Adam B. Goldstein & Neil T. Heffernan

Authors
  1. Ryan S. J. d. Baker
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  2. Adam B. Goldstein
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  3. Neil T. Heffernan
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Editor information

Editors and Affiliations

  1. Human-Computer Interaction Institute, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA

    Vincent Aleven  & Jack Mostow  & 

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

    Judy Kay

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© 2010 Springer-Verlag Berlin Heidelberg

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Baker, R.S.J.d., Goldstein, A.B., Heffernan, N.T. (2010). Detecting the Moment of Learning. In: Aleven, V., Kay, J., Mostow, J. (eds) Intelligent Tutoring Systems. ITS 2010. Lecture Notes in Computer Science, vol 6094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13388-6_7

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  • DOI: https://doi.org/10.1007/978-3-642-13388-6_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13387-9

  • Online ISBN: 978-3-642-13388-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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