Computational methods for evaluating student and group learning histories in intelligent tutoring systems

Authors:
Carole Beal;Paul Cohen
Affiliations:
USC Information Sciences Institute;USC Information Sciences Institute
Venue:
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Year:
2005

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Abstract

Intelligent tutoring systems customize the learning experiences of students. Because no two students have precisely the same learning history, traditional analytic techniques are not appropriate. This paper shows how to compare the learning histories of students and how to compare groups of students in different experimental conditions. A class of randomization tests is introduced and illustrated with data from the AnimalWatch ITS project for elementary school arithmetic.