Umarani Pappuswamy

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The Why2-Atlas system teaches qualitative physics by having students write paragraph-long explanations of simple mechanical phenomena. The tutor uses deep syntactic analysis and abductive theorem proving to convert the student's essay to a proof. The proof formalizes not only what was said, but the likely beliefs behind what was said. This allows the tutor(More)
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(More)
In this paper, we describe a multi-tier Natural Language (NL) clustering approach to text classification for classifying students' essays for tutoring applications. The main task of the classifier is to map the students' essay statements into target concepts, namely physics principles and misconceptions. A simplèBag-Of-Words (BOW)' classifier using a(More)
The Why2-Atlas tutoring system (VanLehn et al. 2002) presents students with qualitative physics questions and encourages them via natural language dialogue to explain their answers. We describe changes we are making to the current version of Why2-Atlas to better utilize a proof-based representation of student essays for motivating dialogue. The abductive(More)
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 an approach that combines a statistical classi-fier, multiple semantic parsers and a formal reasoner for achieving a deeper(More)
When dealing with a natural language interaction about a formal domain , a number of phenomena occur. They include interspersing natural language with formulas, various degrees of formality, and conveying the logical structure of an essay. Capturing these phenomena, to some extent, is necessary for providing relevant tutoring feedback. In this paper we(More)
In this paper we describe a part of the Why2-Atlas tutoring system that models students' reasoning in the domain of qualitative physics. The main goals of the model are (1) to evaluate correctness of the student's essay, and, in case the essay contains errors, (2) to direct remedial tutoring actions according to plausible errors in the student's reasoning.(More)
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(More)
This paper describes a supervised three-tier clustering method for classifying students' essays of qualitative physics in the Why2-Atlas tutoring system. Our main purpose of categorizing text in our tutoring system is to map the students' essay statements into principles and misconceptions of physics. A simplèbag-of-words' representation using a naïve-bayes(More)