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

被引：0
|
作者：
Beal, Carole
Cohen, Paul
机构：
来源：
ARTIFICIAL INTELLIGENCE IN EDUCATION: SUPPORTING LEARNING THROUGH INTELLIGENT AND SOCIALLY INFORMED TECHNOLOGY | 2005年 / 125卷
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D O I：
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中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Intelligent tutoring systems customize the learning experiences of students. Because no two students have precisely the same learning history, traditional analytic techniques tine 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 Animal Watch ITS project for elementary school arithmetic.
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页码：80 / 87
页数：8
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