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Item Response Model with Lower Order Parameters for Peer Assessment

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Artificial Intelligence in Education (AIED 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9112))

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Abstract

Peer assessment has become popular in recent years. However, in peer assessment, a problem remains that reliability depends on the rater characteristics. For this reason, some item response models that incorporate rater parameters have been proposed. However, in previous models, the parameter estimation accuracy decreases as the number of raters increases because the number of rater parameters increases drastically. To solve that problem, this article presents a proposal of a new item response model for peer assessment that incorporates rater parameters to maintain as few rater parameters as possible.

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Author information

Authors and Affiliations

  1. Nagaoka University of Technology, Niigata, Japan

    Masaki Uto

  2. University of Electro-Communications, Tokyo, Japan

    Maomi Ueno

Authors
  1. Masaki Uto
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  2. Maomi Ueno
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Corresponding author

Correspondence to Masaki Uto .

Editor information

Editors and Affiliations

  1. University of British Columbia, Vancouver, British Columbia, Canada

    Cristina Conati

  2. Computer Science Department, Worcester Polytechnic Institute, Worcester, Massachusetts, USA

    Neil Heffernan

  3. Department of Computer Science and Software Engineering, University of Canterbury, Christchurch, New Zealand

    Antonija Mitrovic

  4. E.T.S.I. Informática, Universidad National de Educacion a Distancia, Madrid, Spain

    M. Felisa Verdejo

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© 2015 Springer International Publishing Switzerland

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Uto, M., Ueno, M. (2015). Item Response Model with Lower Order Parameters for Peer Assessment. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_119

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  • DOI: https://doi.org/10.1007/978-3-319-19773-9_119

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19772-2

  • Online ISBN: 978-3-319-19773-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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