This is the repository of FAST, an efficient toolkit for modeling time-changing student performance ([González-Brenes, Huang, Brusilovsky et al, 2014] (http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf)). FAST is alterantive to the [BNT-SM toolkit] (http://www.cs.cmu.edu/~listen/BNT-SM/), a toolkit that requires the researcher to design a different different Bayes Net for each feature set they want to prototype. The FAST toolkit is up to 300x faster than BNT-SM, and much simpler to use.

We presented the model in the 7th International Conference on Educational Data Mining (2014) (see [slides] (http://www.cs.cmu.edu/~joseg/files/fast_presentation.pdf) ), where it was selected as one the top 5 paper submissions.

FAST learns per parameters for each skill using an HMM with Features ([Berg-Kirpatrick et al, 2010] (http://www.cs.berkeley.edu/~tberg/papers/naaclhlt2010.pdf)).

1. Download the latest release [here] (https://github.com/ml-smores/fast/releases).
2. Decompress the file. It includes sample data for getting you started quickly.
3. Open a terminal and type (you need to be in the same directory as the fast-2.1.1-final.jar file in your console, which can be achieved by the cd command):
   ``` java -jar fast-2.1.1-final.jar ++data/IRT_exp/FAST+IRT1.conf ````

Congratulations! You just trained a student model (with IRT features) using state of the art technology.

Please see the Wiki for more information.

Please cite our work (and provide the link https://github.com/ml-smores/fast) if you use our tool in your published papers: González-Brenes, J. P., Huang, Y., & Brusilovsky, P. (2014). General features in knowledge tracing: applications to multiple subskills, temporal item response theory, and expert knowledge. In Proc. 7th Int. Conf. on Educational Data Mining (pp. 84-91).

We would love to hear your feedback. Please [email us] (mailto:ml-smores@googlegroups.com)!

Thanks, Yun, Jose, and Peter