Neil T. Heffernan
David C. Brown
Cognitive Science is interested in being able to develop methodologies for analyzing human learning and performance data. Intelligent tutoring systems need good cognitive models that can predict student performance. Cognitive models of human processing are also useful in tutoring because well-designed curriculums need to understand the common components of knowledge that students need to be able to employ. A common concern is being able to predict when transfer should happen. We describe a methodology first used by Koedinger that uses empirical data and cognitively principled task analysis to evaluate the fit of cognitive models. This methodology seems particularly useful when you are trying to find evidence for“hidden" knowledge components, which are hard to assess because they are confounded with accessing other knowledge components. We present this methodology as well as an illustration showing how we are trying to use this method to answer an important cognitive science issue.
Worcester Polytechnic Institute
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Croteau, Ethan, "Evaluating Predictions of Transfer and Analyzing Student Motivation" (2004). Masters Theses (All Theses, All Years). 556.
Dialog, Learning Gain, Web-Based Evaluations, Empirical Results, Student Motivation, Mathematics Education, Model-Tracing Tutors, Tutoring Strategy Evaluation, Intelligent Tutoring, Cognitive learning, Intelligent tutoring systems