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ERIC Number: ED562499
Record Type: Non-Journal
Publication Date: 2015-Jul
Pages: 7
Abstractor: As Provided
Reference Count: 22
ISBN: N/A
ISSN: N/A
Behavioral Feature Extraction to Determine Learning Styles in e-Learning Environments
Fatahi, Somayeh; Moradi, Hadi; Farmad, Elaheh
International Association for Development of the Information Society, Paper presented at the International Association for Development of the Information Society (IADIS) International Conference on e-Learning (Las Palmas de Gran Canaria, Spain, Jul 21-24, 2015)
Learning Style (LS) is an important parameter in the learning process. Therefore, learning styles should be considered in the design, development, and implementation of e-learning environments. Consequently, an important capability of an e-learning system could be the automatic determination of a student's learning style. In this paper, a set of features which are important in extracting the learning style automatically from students' behavior has been determined. These features, which are recognized based on Myers-Briggs Type Indicator's (MBTI), play a key role in predicting learning styles in an online course. The features are determined and ranked using pattern recognition techniques, such as K-means clustering algorithm, to show which features can be better to separate learning style dimensions. The results show several features can be used to predict learning styles with high precision. [For the complete proceedings, see ED562095.]
International Association for the Development of the Information Society. e-mail: secretariat@iadis.org; Web site: http://www.iadisportal.org
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Iran
Identifiers - Assessments and Surveys: Myers Briggs Type Indicator