Abductive theorem proving for analyzing student explanations to guide feedback in intelligent tutoring systems

被引：13

作者：

Makatchev M. ^{[1
]}

Jordan P.W. ^{[1
]}

VanLehn K. ^{[1
]}

机构：

[1] Lrng. Res. and Development Center, University of Pittsburgh, Pittsburgh

来源：

Journal of Automated Reasoning | 2004年 / 32卷 / 03期

基金：

美国国家科学基金会;

关键词：

Abductive reasoning; Intelligent tutoring systems; Qualitative physics;

D O I：

10.1023/B:JARS.0000044823.50442.cd

中图分类号：

学科分类号：

摘要：

The Why2-Atlas tutoring system presents students with qualitative physics questions and encourages them to explain their answers through 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 motivate and describe how the system creates and uses a deeper proof-based representation of student essays in order to provide students with substantive feedback on their explanations. We describe in detail the abductive reasoner, Tacitus-lite+, that we use within the tutoring system. We also discuss evaluation results for an early version of the Why2-Atlas system and a subsequent evaluation of the theorem-proving module. We conclude with the discussion of work in progress and additional future work for deriving more benefits from a proof-based approach for tutoring applications.

引用

页码：187 / 226

页数：39

共 68 条

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