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ERIC Number: ED539076
Record Type: Non-Journal
Publication Date: 2009-Jul
Pages: 10
Abstractor: As Provided
Reference Count: 14
ISBN: N/A
ISSN: N/A
Predicting Correctness of Problem Solving from Low-Level Log Data in Intelligent Tutoring Systems
Cetintas, Suleyman; Si, Luo; Xin, Yan Ping; Hord, Casey
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 proposes a learning based method that can automatically determine how likely a student is to give a correct answer to a problem in an intelligent tutoring system. Only log files that record students' actions with the system are used to train the model, therefore the modeling process doesn't require expert knowledge for identifying domain specific skills that are needed to solve the problem or students' possible solution methods etc. The model utilizes a set of performance features, problem features, time and mouse movement features and is compared to i) a model that utilizes performance and problem features, ii) a model that uses performance, problem and time features. In order to address data sparseness problem, a robust Ridge Regression algorithm is designed to estimate model parameters. An extensive set of experiment results demonstrate the power of using multiple types of evidence as well as the robust Ridge Regression algorithm. (Contains 3 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 - Research; Speeches/Meeting Papers
Education Level: Elementary Education; Elementary Secondary Education; Grade 4
Audience: N/A
Language: English
Sponsor: National Science Foundation
Authoring Institution: International Working Group on Educational Data Mining