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)).
Congratulations! You just trained a student model (with IRT features) using state of the art technology.
Please see the Wiki for more information.
We would love to hear your feedback. Please [email us] (mailto:[email protected])!
Thanks, Yun, Jose, and Peter
