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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 21st FLAIRS Conference |
| Number of pages | 6 |
| Publication status | Published - 2008 |
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