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Evaluating Bayesian networks’ precision for detecting students’ learning styles
ARTICLE

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Computers & Education Volume 49, Number 3, ISSN 0360-1315 Publisher: Elsevier Ltd

Abstract

Students are characterized by different learning styles, focusing on different types of information and processing this information in different ways. One of the desirable characteristics of a Web-based education system is that all the students can learn despite their different learning styles. To achieve this goal we have to detect how students learn: reflecting or acting; steadily or in fits and starts; intuitively or sensitively. In this work, we evaluate Bayesian networks at detecting the learning style of a student in a Web-based education system. The Bayesian network models different aspects of a student behavior while he/she works with this system. Then, it infers his/her learning styles according to the modeled behaviors. The proposed Bayesian model was evaluated in the context of an Artificial Intelligence Web-based course. The results obtained are promising as regards the detection of students’ learning styles. Different levels of precision were found for the different dimensions or aspects of a learning style.

Citation

Garcia, P., Amandi, A., Schiaffino, S. & Campo, M. (2007). Evaluating Bayesian networks’ precision for detecting students’ learning styles. Computers & Education, 49(3), 794-808. Elsevier Ltd. Retrieved May 21, 2019 from .

This record was imported from Computers & Education on April 18, 2013. Computers & Education is a publication of Elsevier.

Full text is availabe on Science Direct: http://www.eric.ed.gov/ERICWebPortal/detail?accno=EJ765075

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