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

  • Shu-Chuan Shih
  • Bor-Chen Kuo
Part of the Lecture Notes in Computer Science book series (LNCS, 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Shu-Chuan Shih
    • 1
  • Bor-Chen Kuo
    • 2
  1. 1.Department of Mathematics EducationNational Taichung Teachers CollegeTaichungTaiwan
  2. 2.Graduate School of Educational Measurement and StatisticsNational Taichung Teachers CollegeTaichungTaiwan

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