ERIC Number: EJ1182968
Record Type: Journal
Publication Date: 2018-Jul
Pages: 19
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1360-2357
EISSN: N/A
An Empirical Study of Using Sequential Behavior Pattern Mining Approach to Predict Learning Styles
Fatahi, Somayeh; Shabanali-Fami, Faezeh; Moradi, Hadi
Education and Information Technologies, v23 n4 p1427-1445 Jul 2018
The learning style of a learner is an important parameter in his learning process. Therefore, learning styles should be considered in the design, development, and implementation of e-learning environments to increase learners' performance. Thus, it is important to be able to automatically determine learning styles of learners in an e-learning environment. In this paper, we propose a sequential pattern mining approach to extract frequent sequential behavior patterns, which can separate learners with different learning styles. In this research, in order to recognize learners' learning styles, system uses the Myers-Briggs Type Indicator's (MBTI). The approach has been implemented and tested in an e-learning environment and the results show that learning styles of learners can be predicted with high accuracy. We show that learners with similar learning styles have similar sequential behavior patterns in interaction with an e-learning environment. A lot of frequent sequential behavior patterns were extracted which some of them have a meaningful relation with MBTI dimensions.
Descriptors: Cognitive Style, Educational Technology, Technology Uses in Education, Student Behavior, Predictor Variables, Personality Measures, Behavior Patterns
Springer. Available from: Springer Nature. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Assessments and Surveys: Myers Briggs Type Indicator
Grant or Contract Numbers: N/A