Carnegie Mellon University, Massachusetts Institute of Technology, Stanford University, University of Memphis. Copyright 2016. All Rights Reserved.

LearnSphere is co-developed by the LearnLab – a flagship project of Carnegie Mellon's Simon Initiative. It is community software infrastructure for sharing, analysis, and collaboration of/around educational data. LearnSphere integrates existing and new educational data infrastructures to offer a world class repository of education data.

Tigris is a workflow authoring tool which is part of the community software infrastructure being built for the LearnSphere project. The platform provides a way to create custom analyses and interact with new as well as existing data formats and repositories.

Bayesian Knowledge Tracing (BKT) workflow component is one of the building blocks in Tigris. It implements student modeling approach based in Hidden Markov Models. BKT can be run on most of the LearnSphere datasets without prior adaptation.

Bayesian Knowledge Training (BKT) is a user modeling approach widely use in the area of Intelligent Tutoring Systems (ITS). In ITS, it is customary to tag problems and problem steps students are working on with knowledge quanta (also called skills). When a student is attempting to solve a problem or a problem step, they are said to practice a skill (skills). A student model (BKT being one) performs deductions on whether the skill(s) is (are) mastered given a sequence of correct and incorrect attempts to solve problems (problem steps). BKT uses a formalism of a Hidden Markov Model to make the estimation of student's skill mastery. In BKT, there are two types of nodes: binary state nodes capture skill mastery (one per skill) that assume values of mastered or not mastered and binary observation nodes (one per skill) that assume values of correct or incorrect.

Each skill in standard BKT has the following parameters.

1. p-init or p(Lo) - is a probability the skill was known a priori,
2. p-learn or p(T) - is a probability the skill will transition into mastered state after a practice attempt,
3. p-forget or p(F) - is a probability that the skill will transition into not mastered state after a practice attempt. Traditionally, p-forget is set to zero and is not counted towards the total number of parameters.
4. p-slip or p(S) - is a probability that a mastered skill is applied incorrectly, and
5. p-guess or p(G) - is a probability that unmastered skill will be applied correctly.

For more details on BKT refer to the original paper on it [1]. For the documentation on the core utility that performs the fitting of the BKT model refer to [2]. For implementation details and theoretical issues of fitting hidden Markov models, refer to [3].

1. Corbett, A. T., and Anderson, J. R.: Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253-278. (1995)

2. Yudelson, M. HMM-scalable – A Tool for fitting Hidden Markov Models models at scale. http://yudelson.info/hmm-scalable

3. Levinson, S. E., Rabiner, L. R., and Sondhi, M. M.: An Introduction to the Application of the Theory of Probabilistic Functions of a Markov Process to Automatic Speech Recognition. Bell System Technical Journal, 62(4): 1035-1074. (1983)

1. Ant 1.9 or greater
2. Java Enterprise Edition Software Development Kit (J2EE SDK)
3. Eclipse or Cygwin.
4. Clone the GitHub repository using git clone https://github.com/PSLCDataShop/WorkflowComponents WorkflowComponents command.
5. BKT contains executables which may need to be rebuilt for your system.
   • From the command-line in WorkflowComponents/AnalysisBkt/program/standard-bkt-public-standard-bkt folder issue the makecommand.
   • Then, copy the predicthmm.exe and trainhmm.exe to the AnalysisBkt/program directory.

See WorkflowComponents/Workflow Components.docx for detailed information on creating, modifying, or running components.

1. File -> Import -> General -> Existing Projects into Workspace.
2. Choose any component directory from your newly imported git clone, i.e. WorkflowComponents/<AnyComponent>.
3. Click 'Finish'.
4. In the Ant view (Windows -> Show View -> Ant), add the desired component's build.xml to your current buildfiles, e.g. <AnyComponent>/build.xml.
5. Double click the ant task runToolTemplate. The component should produce example XML output if it is setup correctly.

NB: For debugging, you may wish to add the jars in the directory WorkflowComponents/CommonLibraries to your build path.

1. Change to your WorkflowComponents directory, e.g. /cygdrive/c/your_workspace/<AnyComponent>/
2. Issue the command ant -p to get a list of ant tasks
3. Issue ant runComponent to run the component with the included example data

Modify the dir variable in WorkflowComponents/build.sh to match your WorkflowComponents path, then run the script (requires bash)

Issue the ant dist command.