SimStudent is an interactive learner, who applies inductive logic programming to learn cognitive skills from examples. SimStudent is customizable (or "programmable") so that we can test various hypothesis to identify factors that affect learning and test when and how such factors affect learning. The learning outcome (i.e., learned cognitive skills) is represented as a set of production rules, which is also human readable.

The above characteristics provides us unique opportunities to conduct studies on machine- and human-learning. Currently, we have three research areas as listed below:

Teachable Peer Learner

What if we ask human students to teach SimStudent? It is well known that students learn by teaching others. We can build an on-line learning environment where SimStudent acts as a peer tutoree and have human students tutor SimStudent. Such a learning environment also provides us opportunities to study more about the theories  of tutor learning and other social and motivational factors of learning. See our REESE page at ARC to study more about our findings.  An article at Research.gov introducing our study.

Computational Model of Learning

SimStudents can be used as simulated students with which researchers can conduct various controlled studies to explore theories of learning. For example, we have compared different learning strategies, learning from worked-out examples vs. learning by being tutored. We have also studied how differences in prior knowledge affect learning by comparing learning outcomes from “good” SimStudent, who has stronger prior knowledge of prerequisite concepts, and “poor” SimStudent, who has weaker, more perceptually-oriented prior knowledge. See PSLC Theory Wiki for details of the Error Analysis study.

Intelligent Authoring

Use of SimStudent within the Cognitive Tutor Authoring Tools (CTAT) allows authors to build a Cognitive Tutor (especially the so-called expert model that represents domain principles) simply by teaching SimStudent how to solve problems. SimStudent is available as a built-in component for CTAT, which can be downloaded at the CTAT project web page.

Last Updated January 24, 2010