Diagnosing natural language answers to support adaptive tutoring
Proceedings of the 21st FLAIRS Conference
Date
01/11/2010Author
Dzikovska, Myroslava
Campbell, Gwendolyn
Callaway, Charles
Steinhauser, Natalie
Farrow, Elaine
Moore, Johanna D.
Butler, Leslie
Matheson, Colin
Metadata
Abstract
Understanding answers to open-ended explanation
questions is important in intelligent tutoring systems.
Existing systems use natural language techniques in
essay analysis, but revert to scripted interaction with
short-answer questions during remediation, making
adapting dialogue to individual students difficult. We
describe a corpus study that shows that there is a relationship
between the types of faulty answers and the
remediation strategies that tutors use; that human tutors
respond differently to different kinds of correct answers;
and that re-stating correct answers is associated
with improved learning. We describe a design for a diagnoser
based on this study that supports remediation in
open-ended questions and provides an analysis of natural
language answers that enables adaptive generation
of tutorial feedback for both correct and faulty answers.