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NLP Techniques in Intelligent Tutoring Systems

NLP Techniques in Intelligent Tutoring Systems

Chutima Boonthum-Denecke, Irwin B. Levinstein, Danielle S. McNamara, Joseph P. Magliano, Keith K. Millis
Copyright: © 2009 |Pages: 6
ISBN13: 9781599048499|ISBN10: 1599048493|EISBN13: 9781599048505
DOI: 10.4018/978-1-59904-849-9.ch183
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MLA

Boonthum-Denecke, Chutima, et al. "NLP Techniques in Intelligent Tutoring Systems." Encyclopedia of Artificial Intelligence, edited by Juan Ramón Rabuñal Dopico, et al., IGI Global, 2009, pp. 1253-1258. https://doi.org/10.4018/978-1-59904-849-9.ch183

APA

Boonthum-Denecke, C., Levinstein, I. B., McNamara, D. S., Magliano, J. P., & Millis, K. K. (2009). NLP Techniques in Intelligent Tutoring Systems. In J. Rabuñal Dopico, J. Dorado, & A. Pazos (Eds.), Encyclopedia of Artificial Intelligence (pp. 1253-1258). IGI Global. https://doi.org/10.4018/978-1-59904-849-9.ch183

Chicago

Boonthum-Denecke, Chutima, et al. "NLP Techniques in Intelligent Tutoring Systems." In Encyclopedia of Artificial Intelligence, edited by Juan Ramón Rabuñal Dopico, Julian Dorado, and Alejandro Pazos, 1253-1258. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-59904-849-9.ch183

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Abstract

Many Intelligent Tutoring Systems (ITSs) aim to help students become better readers. The computational challenges involved are (1) to assess the students’ natural language inputs and (2) to provide appropriate feedback and guide students through the ITS curriculum. To overcome both challenges, the following non-structural Natural Language Processing (NLP) techniques have been explored and the first two are already in use: word-matching (WM), latent semantic analysis (LSA, Landauer, Foltz, & Laham, 1998), and topic models (TM, Steyvers & Griffiths, 2007). This article describes these NLP techniques, the iSTART (Strategy Trainer for Active Reading and Thinking, McNamara, Levinstein, & Boonthum, 2004) intelligent tutor and the related Reading Strategies Assessment Tool (R-SAT, Magliano et al., 2006), and how these NLP techniques can be used in assessing students’ input in iSTART and R-SAT. This article also discusses other related NLP techniques which are used in other applications and may be of use in the assessment tools or intelligent tutoring systems.

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