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Using Bayesian Networks for Modeling Students’ Learning Bugs and Sub-skills

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Book cover Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3681))

Abstract

This studyexplores the efficiency of using Bayesian networks for modeling assessment data and identifying bugs and sub-skills in addition and subtraction with decimals after students have learned the related contents. Four steps are involved in this study: developing the student model based on Bayesian networks that can describe the relations between bugs and sub-skills, constructing and administering test items in order to measure the bugs and sub-skills, estimating the network parameters using the training sample and applying the generated networks to bugs and sub-skills diagnosis using the testing sample, and assessing the effectiveness of the generated Bayesian network models work in predicting the existence of bugs and sub-skills. The results show that using Bayesian networks to diagnose the existence of bugs and sub-skills of students can get good performance.

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Author information

Authors and Affiliations

  1. Department of Mathematics Education, National Taichung Teachers College, Taichung, Taiwan

    Shu-Chuan Shih

  2. Graduate School of Educational Measurement and Statistics, National Taichung Teachers College, Taichung, Taiwan

    Bor-Chen Kuo

Authors
  1. Shu-Chuan Shih
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  2. Bor-Chen Kuo
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Editor information

Editors and Affiliations

  1. School of Business, La Trobe University, 3086, Melbourne, Victoria, Australia

    Rajiv Khosla

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, Moulsecoomb, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2005 Springer-Verlag Berlin Heidelberg

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Shih, SC., Kuo, BC. (2005). Using Bayesian Networks for Modeling Students’ Learning Bugs and Sub-skills. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552413_11

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  • DOI: https://doi.org/10.1007/11552413_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28894-7

  • Online ISBN: 978-3-540-31983-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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