Deeper Natural Language Processing for Evaluating Student Answers in Intelligent Tutoring Systems
Abstract
This paper addresses the problem of evaluating stu-dents ’ answers in intelligent tutoring environments with mixed-initiative dialogue by modelling it as a textual entailment problem. The problem of meaning represen-tation and inference is a pervasive challenge in any inte-grated intelligent system handling communication. For intelligent tutorial dialogue systems, we show that en-tailment 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.
Cite
Text
Rus and Graesser. "Deeper Natural Language Processing for Evaluating Student Answers in Intelligent Tutoring Systems." AAAI Conference on Artificial Intelligence, 2006.Markdown
[Rus and Graesser. "Deeper Natural Language Processing for Evaluating Student Answers in Intelligent Tutoring Systems." AAAI Conference on Artificial Intelligence, 2006.](https://mlanthology.org/aaai/2006/rus2006aaai-deeper/)BibTeX
@inproceedings{rus2006aaai-deeper,
title = {{Deeper Natural Language Processing for Evaluating Student Answers in Intelligent Tutoring Systems}},
author = {Rus, Vasile and Graesser, Arthur C.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2006},
pages = {1495-1500},
url = {https://mlanthology.org/aaai/2006/rus2006aaai-deeper/}
}