Search
 
loader loading subjects...
cover
Computational methods for evaluating student and group learning histories in intelligent tutoring systems
Carole Beal, Paul Cohen
80 - 87
Frontiers in Artificial Intelligence and Applications
Volume 125: Artificial Intelligence in Education
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.

$35.00 / €27.50 / £22.00
Add PDF to cart