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Computational Aspects of the Intelligent Tutoring System MetaTutor
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Author(s): Mihai Lintean (The University of Memphis, USA), Vasile Rus (The University of Memphis, USA), Zhiqiang Cai (The University of Memphis, USA), Amy Witherspoon-Johnson (Arizona State University, USA), Arthur C. Graesser (The University of Memphis, USA)and Roger Azevedo (McGill University, Canada)
Copyright: 2012
Pages: 14
Source title:
Applied Natural Language Processing: Identification, Investigation and Resolution
Source Author(s)/Editor(s): Philip M. McCarthy (The University of Memphis, USA)and Chutima Boonthum-Denecke (Hampton University, USA)
DOI: 10.4018/978-1-60960-741-8.ch014
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Abstract
We present in this chapter the architecture of the intelligent tutoring system MetaTutor that trains students to use metacognitive strategies while learning about complex science topics. The emphasis of this chapter is on the natural language components. In particular, we present in detail the natural language input assessment component used to detect students’ mental models during prior knowledge activation, a metacognitive strategy, and the micro-dialogue component used during sub-goal generation, another metacognitive strategy in MetaTutor. Sub-goal generation involves sub-goal assessment and feedback provided by the system. For mental model detection from prior knowledge activation paragraphs, we have experimented with three benchmark methods and six machine learning algorithms. Bayes Nets, in combination with a word-weighting method, provided the best accuracy (76.31%) and best human-computer agreement scores (kappa=0.63). For sub-goal assessment and feedback, a taxonomy-driven micro-dialogue mechanism yields very good to excellent human-computer agreement scores for sub-goal assessment (average kappa=0.77).
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