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. BNT-SM allows researchers to easily explore different hypothesis with respect to the knowledge representation in a student model. For example, by varying the graphical structure of a Bayesian network, we examined how tutoring intervention can affect students' knowledge state - whether the intervention is likely to scaffold or to help students to learn.
Before we download BNT-SM, we like to thank Kevin Murphy for his kindness in distributing Bayes Net Toolkit (BNT), which BNT-SM based and heavily depended on. For those of you who are proficient in coding and would like to go to the low level BNT code, BNT can be downloaded from Source Forge. Kevin Murphy also has a nice tutorial to BNT and Bayes nets in general.
BNT-SM can be downloaded from here.
Now, with BNT-SM downloaded and extracted, launch Matlab and do
>> cd src >> setup >> cd ../model/kt >> [property evidence hash_bnet] = RunBnet('property.xml');
A Walk-through Example of modeling Knowledge Tracing with BNT-SM.
An Example of tracing multiple subskills with BNT-SM.
Chang, K., Beck, J., Mostow, J., & Corbett, A. (2006, June 26-30). A Bayes Net Toolkit for Student Modeling in Intelligent Tutoring Systems. Proceedings of the 8th International Conference on Intelligent Tutoring Systems, Jhongli, Taiwan, 104-113. Click here for .pdf file.
If you are running LR-DBN with BTN-SM, please cite:
Xu, Y., & Mostow, J. (2011, July 6-8). Using Logistic Regression to Trace Multiple Subskills in a Dynamic Bayes Net. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, & J. Stamper (Eds.), Proceedings of the 4th International Conference on Educational Data Mining (pp. 241-245). Eindhoven, Netherlands. Click here for .pdf file.