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A Rough Set Methodology to Support Learner Self-Assessment in Web-Based Distance Education

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Book cover Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2639))

Abstract

With the prevalence and explosive growth of distance education via the World Wide Web, many efforts are dedicated to make distance education more effective. We present a Rough Set model to provide an instrument for learner self-assessment when taking courses delivered via the World Wide Web. The Rough Set Based Inductive Learning Algorithm generates definite and probabilistic(general) rules, which are used to provide feedback to learners.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada, S4S 0A2

    Hongyan Geng & Brien Maguire

Authors
  1. Hongyan Geng
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  2. Brien Maguire
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Editor information

Editors and Affiliations

  1. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, P.R. China

    Guoyin Wang

  2. Department of Computer Science, Nanchang University, Nanchang, 330029, P.R. China

    Qing Liu

  3. Department of Computer Science, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada

    Yiyu Yao

  4. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

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© 2003 Springer-Verlag Berlin Heidelberg

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Geng, H., Maguire, B. (2003). A Rough Set Methodology to Support Learner Self-Assessment in Web-Based Distance Education. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_42

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  • DOI: https://doi.org/10.1007/3-540-39205-X_42

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-14040-5

  • Online ISBN: 978-3-540-39205-7

  • eBook Packages: Springer Book Archive

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