A useful prediction variable for student models: cognitive
Ivon Arroyo, Joseph E. Beck, Klaus Schultz, Beverly Park Woolf
Computer Science Department and School of Education, University of Massachusetts, Amherst
Making a realistic update of a user model based on evidence in the environment is
not an easy task, unless a great deal of time with a large variety of users is available.
Creating general categories of users that behave in a certain way is important for any
kind of user model. To obtain such a broad classification we need to understand
general factors that influence user behavior. We describe the use of pretests to
measure cognitive development of student users and how this factor is input to a
student model. We describe how measures of cognitive ability enhance the
predictive power of a student model in an intelligent tutoring system for a
population of young (elementary school) students.
Keywords: Cognitive resources, Piaget, student modeling.
Computer Science Department, University of Massachusetts, Amherst, MA 01003
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