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

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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.