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The Writing-Pal: Natural Language Algorithms to Support Intelligent Tutoring on Writing Strategies
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Author(s): Danielle S. McNamara (Arizona State University, USA), Roxanne Raine (The University of Memphis, USA), Rod Roscoe (Arizona State University, USA), Scott A. Crossley (Georgia State University, USA), G. Tanner Jackson (Arizona State University, USA), Jianmin Dai (Arizona State University, USA), Zhiqiang Cai (The University of Memphis, USA), Adam Renner (The University of Memphis, USA), Russell Brandon (Arizona State University, USA), Jennifer L. Weston (Arizona State University, USA), Kyle Dempsey (Mississippi University for Women, USA), Diana Carney (The University of Memphis, USA), Susan Sullivan (The University of Memphis, USA), Loel Kim (The University of Memphis, USA), Vasile Rus (The University of Memphis, USA), Randy Floyd (The University of Memphis, USA), Philip M. McCarthy (The University of Memphis, USA)and Arthur C. Graesser (The University of Memphis, USA)
Copyright: 2014
Pages: 14
Source title:
K-12 Education: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-4502-8.ch045
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Abstract
The Writing-Pal (W-Pal) is an intelligent tutoring system (ITS) that provides writing strategy instruction to high school students and entering college students. One unique quality of W-Pal is that it provides feedback to students’ natural language input. Thus, much of our focus during the W-Pal project has been on Applied Natural Language Processing (ANLP). This chapter describes W-Pal and various NLP projects geared toward providing automated feedback to students’ writing during writing strategy training and practice.
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