TY  - JOUR
T1  - Fuzzy Case-based Approach for Detection of Learning Styles:
A Proposed Model
AU - Rahayu Ngatirin, Nor AU - Zainol, Zurinahni AU - Abdul Rashid, Nur`Aini 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 2
SP  - 321
EP  - 327
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.321.327
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.321.327
KW  - Learning style
KW  -personality
KW  -fuzzy logic
KW  -case-based reasoning
KW  -classification
KW  -students
AB  - A learning style refers to the way an individual learns. The traditional 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. The automatic detection of learning styles is proven to be beneficial to
students as it can supply them with learning materials according to their individual preferences. In this study
we propose a hybrid approach that combines fuzzy logic and case-based reasoning method to classify students
according to their learning styles and preferences. In the context of modeling the learning styles, a student
model will be constructed based on the information of student’s performance during the online course,
personality and their gender. Within this study, we intend to outline our proposed model following the
felder-silverman model of learning styles and the Big Five model of personality.
ER  -