Predicting student help-request behavior in an intelligent tutor for reading
Abstract
This paper describes our efforts at constructing a fine-grained student model in Project LISTEN's intelligent tutor for reading. Reading is different from most domains that have been studied in the intelligent tutoring community, and presents unique challenges. Constructing a model of the user from voice input and mouse clicks is difficult, as is constructing a model when there is not a well-defined domain model. We use a database describing student interactions with our tutor to train a classifier that predicts whether students will click on a particular word for help with 83.2% accuracy. We have augmented the classifier with features describing properties of the word's individual graphemes, and discuss how such knowledge can be used to assess student skills that cannot be directly measured.
BibTeX
@conference{Beck-2003-8676,author = {Joseph E. Beck and P. Jia and J. Sison and Jack Mostow},
title = {Predicting student help-request behavior in an intelligent tutor for reading},
booktitle = {Proceedings of 9th International Conference on User Modeling (UM '03)},
year = {2003},
month = {June},
pages = {303 - 312},
}