Deeper Natural Language Processing for Evaluating Student Answers in Intelligent Tutoring Systems

Vasile Rus, Arthur C. Graesser

This paper addresses the problem of evaluating students' answers in intelligent tutoring environments with mixed-initiative dialogue by modelling it as a textual entailment problem. The problem of meaning representation and inference is a pervasive challenge in any integrated intelligent system handling communication. For intelligent tutorial dialogue systems, we show that entailment cases can be detected at various dialog turns during a tutoring session. We report the performance of a lexico-syntactic approach on a set of entailment cases that were collected from a previous study we conducted with AutoTutor.

Subjects: 13. Natural Language Processing; 11. Knowledge Representation

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