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Improving Peer Assessment Modeling of Teacher’s Grades, The Case of OpenAnswer

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Emerging Technologies for Education (SETE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10108))

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Abstract

Questions with answers are rarely used as e-learning assessment tools because of the resulting high workload for the teacher/tutor that should grade them, which can be mitigated by doing peer-assessment. In OpenAnswer we modeled peer-assessment as a Bayesian network connecting the sub-networks representing any participating student to the corresponding answers of her graded peers. The model has shown good ability to predict (without further info from the teacher) the exact teacher’s grade (ground truth) from the peer grades only, and a very good ability to predict it within 1 mark from the right one. In this paper we explore changes to the OpenAnswer model to improve its predictions. The experimental results, obtained by simulating teacher’s grading on real datasets, show the improved predictions.

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Author information

Authors and Affiliations

  1. Computer Science Department, Sapienza University, Rome, Italy

    Maria De Marsico & Andrea Sterbini

  2. Computer, Control, and Management Eng. Department, Sapienza University, Rome, Italy

    Marco Temperini

Authors
  1. Maria De Marsico
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  2. Andrea Sterbini
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  3. Marco Temperini
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Corresponding author

Correspondence to Andrea Sterbini .

Editor information

Editors and Affiliations

  1. National Yunlin University of Science and Technology, Yunlin, Taiwan

    Ting-Ting Wu

  2. Free University of Bozen-Bolzano, Rome, Italy

    Rosella Gennari

  3. National Cheng-Kung University, Tainan, Taiwan

    Yueh-Min Huang

  4. The Education University of Hong Kong, Hong Kong, Hong Kong

    Haoran Xie

  5. MC Information Multimedia Communication AG, Saarbrücken, Germany

    Yiwei Cao

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De Marsico, M., Sterbini, A., Temperini, M. (2017). Improving Peer Assessment Modeling of Teacher’s Grades, The Case of OpenAnswer. In: Wu, TT., Gennari, R., Huang, YM., Xie, H., Cao, Y. (eds) Emerging Technologies for Education. SETE 2016. Lecture Notes in Computer Science(), vol 10108. Springer, Cham. https://doi.org/10.1007/978-3-319-52836-6_64

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  • DOI: https://doi.org/10.1007/978-3-319-52836-6_64

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52835-9

  • Online ISBN: 978-3-319-52836-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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