Diagnosing Natural Language Answers to Support Adaptive Tutoring

Myroslava O. Dzikovska, Gwendolyn E. Campbell, Charles B. Callaway, Natalie B. Steinhauser, Elaine Farrow, Johanna D. Moore, Leslie A. Butler, Colin Matheson

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.

Subjects: 1.3 Computer-Aided Education; 13. Natural Language Processing

Submitted: Feb 25, 2008

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