• One of the datasets comes from the Cognitive Tutor System called Bridge to Algebra and is from the 2006-2007 school year. This was one of the smaller, development datasets made public as part of the 2010 Knowledge Discover and Data mining competition. In this tutor, students answer algebra problems from their math curriculum which is split into sections. The problems consist of many steps that the students must answer to go to the next problem. A student no longer needs to answer steps of a given skill when the Cognitive Tutor’s Knowledge Tracing model believes the student knows the skill with probability 0.95 or greater. When a student has mastered all the skills in their current section they are allowed to move on to the next. The time for students using this system is determined by teachers. Twelve skills were chosen at random from this dataset for analysis (excluding skills such as “press enter” which do not represent math skills). There were an average of 122 students per skill in this dataset.
• Another dataset is collected from ASSISTments Platform’s Skill Builder problem sets. The ASSISTments Platform is an educational research platform better known for its elearning that provides web based math tutoring to 8th-10th grade students. Unlike the Cognitive Tutor System, students are forced to leave the tutor after 10 questions have been finished in one day and will come back to the tutor in a new day. If a student answers three questions correct in a row, they are “graduated” from the problem set. The help the tutorial provided is consist of a series of questions that break a problem into sub steps. A student can also request a hint, but requesting a hint will mark the student as getting the step wrong in the system. Only answers to the original questions are considered. The largest twelve Skill Builder datasets were selected from the ASSISTments Platform. There was an average of 1,200 students per problem set in this dataset. The highest student count problem sets were selected here because new day events are far more sparse in ASSISTments skill problem sets than the Cognitive Tutor skill problem sets.

The twelve datasets from each tutor were randomly divided into two equal parts by student, one part was used as the training set, the other as the testing set.