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In the Zone: Towards Detecting Student Zoning Out Using Supervised Machine Learning

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Intelligent Tutoring Systems (ITS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 6095))

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Abstract

This paper explores automatically detecting student zoning out while performing a spoken learning task. Standard supervised machine learning techniques were used to create classification models, built on prosodic and lexical features. Our results suggest these features create models that can outperform a Bag of Words baseline.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science, Sennott Square, University of Pittsburgh, Pittsburgh, PA, 15260

    Joanna Drummond & Diane Litman

Authors
  1. Joanna Drummond
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  2. Diane Litman
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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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Drummond, J., Litman, D. (2010). In the Zone: Towards Detecting Student Zoning Out Using Supervised Machine Learning. In: Aleven, V., Kay, J., Mostow, J. (eds) Intelligent Tutoring Systems. ITS 2010. Lecture Notes in Computer Science, vol 6095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13437-1_53

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  • DOI: https://doi.org/10.1007/978-3-642-13437-1_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13436-4

  • Online ISBN: 978-3-642-13437-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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