Journal ID : TRKU-19-08-2020-11025
[This article belongs to Volume - 62, Issue - 08]
Total View : 434

Title : Fuzzy Logic Approach for Learning Styles Detection in E-Learning Based on Student Performance Information

Abstract :

Learning styles can be referred to the ways an individual gathers, processes, and organizes information. Several students prefer learning by doing and practicing, seeing and listening, memorizing and describing, or reasoning logically and intuitively. Learning style has an effect on the learning process and learners’ achievement. The collaborative way to identify learning styles is through a questionnaire or survey. Despite being reliable, these instruments have several shortcomings that hinder the learning style identification such as students are unmotivated to fill out a questionnaire and reluctant to provide information. Thus, to solve these problems, researchers have proposed several approaches to automatically detect learning styles. This paper identifies Felder-Silverman learning style model as a suitable model for learning style detection and proposes to use fuzzy rules to handle the uncertainty in the learning style detection. The evaluation has used the trapezoidal and triangular membership functions based fuzzy logic for 25 students and compared to their results from the Index of Learning Styles questionnaire. The proposed fuzzy inference system obtained 38% similar classification compared to Felder-Silverman learning styles

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