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ERIC Number: ED539089
Record Type: Non-Journal
Publication Date: 2009-Jul
Pages: 10
Abstractor: As Provided
Reference Count: 19
ISBN: N/A
ISSN: N/A
Automatic Detection of Student Mental Models during Prior Knowledge Activation in MetaTutor
Rus, Vasile; Lintean, Mihai; Azevedo, Roger
International Working Group on Educational Data Mining, Paper presented at the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, Jul 1-3, 2009)
This paper presents several methods to automatically detecting students' mental models in MetaTutor, an intelligent tutoring system that teaches students self-regulatory processes during learning of complex science topics. In particular, we focus on detecting students' mental models based on student-generated paragraphs during prior knowledge activation, a self-regulatory process. We describe two major categories of methods and combine each method with various machine learning algorithms. A detailed comparison among the methods and across all algorithms is also provided. The evaluation of the proposed methods is performed by comparing the prediction of the methods with human judgments on a set of 309 prior knowledge activation paragraphs collected from previous experiments with MetaTutor on college students. According to our experiments, a content-based method with word-weighting and Bayes Nets algorithm is the most accurate. (Contains 1 figure and 2 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)," see ED539041.]
International Working Group on Educational Data Mining. Available from: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Publication Type: Reports - Descriptive; Speeches/Meeting Papers
Education Level: High Schools; Higher Education; Postsecondary Education; Secondary Education
Audience: N/A
Language: English
Sponsor: National Science Foundation
Authoring Institution: International Working Group on Educational Data Mining