Rule-Based Cognitive Modeling for Intelligent Tutoring Systems

Abstract

Rule-based cognitive models serve many roles in intelligent tutoring systems (ITS) development. They help understand student thinking and problem solving, help guide many aspects of the design of a tutor, and can function as the “smarts” of a system. Cognitive Tutors using rule-based cognitive models have been proven to be successful in improving student learning in a range of learning domain. The chapter focuses on key practical aspects of model development for this type of tutors and describes two models in significant detail. First, a simple rule-based model built for fraction addition, created with the Cognitive Tutor Authoring Tools, illustrates the importance of a model’s flexibility and its cognitive fidelity. It also illustrates the model-tracing algorithm in greater detail than many previous publications. Second, a rule-based model used in the Geometry Cognitive Tutor illustrates how ease of engineering is a second important concern shaping a model used in a large-scale tutor. Although cognitive fidelity and ease of engineering are sometimes at odds, overall the model used in the Geometry Cognitive Tutor meets both concerns to a significant degree. On-going work in educational data mining may lead to novel techniques for improving the cognitive fidelity of models and thereby the effectiveness of tutors.