Open AccessAn intelligent agent to detect learner's learning style automatically through E-learning system in Saudi Arabia
Author(s) -
Azlina Abdul Aziz,
Alia Abdulrahman Assiri
Publication year - 2017
Publication title -
mağallaẗ al-ʿulūm al-handasiyyaẗ wa-al-tiknūlūğiyā al-maʿlūmāt
Language(s) - English
Resource type - Journals
ISSN - 2522-3321
DOI - 10.26389/ajsrp.a121117
Subject(s) - style (visual arts) , computer science , learning styles , artificial intelligence , preference , machine learning , e learning , preference learning , mathematics education , psychology , mathematics , world wide web , statistics , the internet , archaeology , history
Most of the successful conventional E-learning system lack of automatic detecting of learner learning style based on their preference. An Automatic approach marked as a better approach to characterize learning style because it is based on real student behavior. The purpose of this study is to propose a new literature-based method (Automatic approach) using intelligent agents to identify learner's learning style based on behavior using Felder-Silverman Learning Style Model (FSLSM). The new method implemented in new developed LMS. After obtaining the proposed method result, the result validates and compared with Felder-Silverman Learning Style Model questionnaire (ILS) and García method. After comparing the proposed method with the results of previous studies, the researcher got satisfactory results on precision percentage and the lowest percentage in the results was 66.6 %.