NLP Techniques in Intelligent Tutoring Systems

NLP Techniques in Intelligent Tutoring Systems

Chutima Boonthum-Denecke (Hampton University, USA), Irwin B. Levinstein (Old Dominion University, USA), Danielle S. McNamara (The University of Memphis, USA), Joseph P. Magliano (Northern Illinois University, USA) and Keith K. Millis (The University of Memphis, USA)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-59904-849-9.ch183
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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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Main Focus Of The Chapter

This article presents three non-structural NLP techniques (WM, LSA, and TM) which are currently used or being explored in reading strategies assessment and training applications, particularly, iSTART and R-SAT.

Key Terms in this Chapter

Kullback Leibler Distance (KL-distance): A natural distance function from a “true” probability distribution to a “target” probability distribution. It can be interpreted as the expected extra message-length per datum due to using a code based on the wrong (target) distribution compared to using a code based on the true distribution.

Probabilistic Latent Semantic Analysis (PLSA): A statistical techniques for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and related areas. PLSA evolved from LSA but focuses more on the relationship of topics within documents.

Word Matching (WM): A simple way to compare words. Literal match is done by comparing character by character, while Soundex match transforms each word into a Soundex code, similar to phonetic spelling.

Self-Explanation and Reading Strategy Trainer (SERT): Pedagogy uses five strategies to help students become a better reader. The reading strategies include (1) comprehension monitoring, being aware of one’s own understanding of the text; (2) paraphrasing, or restating the text in different words; (3) elaboration, using prior knowledge or experiences to understand the text (domain-specific knowledge-based inferences) or using common-sense or logic to understand the text (general knowledge based inferences); (4) predictions, predicting what the text will say next; and (5) bridging, understanding the relation between separate sentences of the text.

Latent Semantic Analysis (LSA): A natural language processing technique that analyses relationships between a set of documents and terms within these documents. LSA was created in 1990 for information retrieval and is sometimes called latent semantic indexing (LSI).

Intelligent Tutoring System (ITS): Also called Intelligence Computer-Aided Instruction (ICAI), a personal training assistant that captures the subject matter and teaching expertise and individualize the curriculum to meet each learner’s needs in order to master the subject matter. Its main goal is to provide benefits of the one-on-one instruction: lessons are conducted at the learner’s own pace; practices are interactive so the learner can improve their weaker skills; and real-time question answering clarify learner’s doubts or misunderstanding; and an individualized curriculum based on the learner’s needs.

Protocols: Any verbal input that students or readers produce during a session. This can be a set of explanations or answers to direct questions.

LSA Cosine: A measurement of a relation between two vector-units. A unit can be as small as a word or as large as an entire document. It can be computed using the dot-product of two vectors where each vector is a representation of a unit (word, sentence, paragraph, or whole document).

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