Understanding Complex Natural Language Explanations In Tutorial Applications
نویسندگان
چکیده
We describe the WHY2-ATLAS intelligent tutoring system for qualitative physics that interacts with students via natural language dialogue. We focus on the issue of analyzing and responding to multisentential explanations. We explore an approach that combines a statistical classifier, multiple semantic parsers and a formal reasoner for achieving a deeper understanding of these explanations in order to provide appropriate feedback on them.
منابع مشابه
Discourse Processing for Explanatory Essays in Tutorial Applications
The Why-Atlas tutoring system presents students with qualitative physics questions and encourages them to explain their answers via natural language. Although there are inexpensive techniques for analyzing explanations, we claim that better understanding is necessary for use within tutoring systems. In this paper we describe how Why-Atlas creates and utilizes a proof-based representation of stu...
متن کاملA Natural Language Tutorial Dialogue System for Physics
We describe the WHY2-ATLAS intelligent tutoring system for qualitative physics that interacts with students via natural language dialogue. We focus on the issue of analyzing and responding to multi-sentential explanations. We explore approaches for achieving a deeper understanding of these explanations and dialogue management approaches and strategies for providing appropriate feedback on them.
متن کاملRepresentation and Reasoning for Deeper Natural Language Understanding in a Physics Tutoring System
Students’ natural language (NL) explanations in the domain of qualitative mechanics lie in-between unrestricted NL and the constrained NL of “proper” domain statements. Analyzing such input and providing appropriate tutorial feedback requires extracting information relevant to the physics domain and diagnosing this information for possible errors and gaps in reasoning. In this paper we will des...
متن کاملPilot-Testing a Tutorial Dialogue System That Supports Self-Explanation
Previous studies have shown that self-explanation is an effective metacognitive strategy and can be supported effectively by intelligent tutoring systems. It is plausible however that students may learn even more effectively when stating explanations in their own words and when receiving tutoring focused on their explanations. We are developing the Geometry Explanation Tutor in order to test th...
متن کاملSimulating Tutors with Natural Dialog and Pedagogical Strategies
Studies indicate that human tutors provide the most effective form of instruction known They raise the mean performance about two standard deviations compared to students taught in classrooms. Intelligent tutoring systems offer excellent instruction, but not quite as good as human tutors. The best ones raise performance about one standard deviation above classroom instruction (e.g., Anderson, C...
متن کامل