An empirical study of using sequential behavior pattern mining approach to predict learning styles

An empirical study of using sequential behavior pattern mining approach to predict learning styles
درجه علمی نشریه: 
دوره: 
۲۳
شماره: 
۴
شماره صفحه (از - تا): 
۱۴۲۷-۱۴۴۵
چکیده

Abstract 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 elearning environment.‎ A lot of frequent sequential behavior patterns were extracted which some of them have a meaningful relation with MBTI dimensions.‎
 

استناد: 

Fatahi, Somayeh, Faezeh Shabanali-Fami, and Hadi Moradi.‎ 2018.‎ An empirical study of using sequential behavior pattern mining approach to predict learning styles.‎ Education and Information Technologies ۲۳ (۴): ۱۴۲۷-۱۴۴۵.

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