Using Bayesian Networks for Modeling Students’ Learning Bugs and Sub-skills
- Cite this paper as:
- 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
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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