A Bayes Net Toolkit for Student Modeling in Intelligent Tutoring Systems
This paper describes an effort to model a student’s changing knowledge state during skill acquisition. Dynamic Bayes Nets (DBNs) provide a powerful way to represent and reason about uncertainty in time series data, and are therefore well-suited to model student knowledge. Many general-purpose Bayes net packages have been implemented and distributed; however, constructing DBNs often involves complicated coding effort. To address this problem, we introduce a tool called BNT-SM. BNT-SM inputs a data set and a compact XML specification of a Bayes net model hypothesized by a researcher to describe causal relationships among student knowledge and observed behavior. BNT-SM generates and executes the code to train and test the model using the Bayes Net Toolbox . Compared to the BNT code it outputs, BNT-SM reduces the number of lines of code required to use a DBN by a factor of 5. In addition to supporting more flexible models, we illustrate how to use BNT-SM to simulate Knowledge Tracing (KT) , an established technique for student modeling. The trained DBN does a better job of modeling and predicting student performance than the original KT code (Area Under Curve = 0.610 > 0.568), due to differences in how it estimates parameters.
KeywordsIntelligent Tutor System Student Knowledge Student Modeling Junction Tree Automatic Speech Recognizer
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- 1.Murphy, K.: Bayes Net Toolbox for Matlab, http://bnt.sourceforge.net (Accessed March 21,2006)
- 3.Woolf, B.: Artificial Intelligence in Education. Encyclopedia of Artificial Intelligence, pp. 434–444. John Wiley & Sons, New York (1992)Google Scholar
- 5.Dean, T., Kanazawa, K.: A model for reasoning about persistence and causation. International Journal of Computational Intelligence 5, 142–150 (1989)Google Scholar
- 6.Reye, J.: Student modeling based on belief networks. International Journal of Artificial Intelligence in Education 14, 1–33 (2004)Google Scholar
- 8.Mostow, J., Aist, G.: Evaluating tutors that listen: An overview of Project LISTEN. In: Forbus, K., Feltovich, P. (eds.) Smart Machines in Education, pp. 169–234. MIT/AAA Press, Menlo Park (2001)Google Scholar