Abstract—The problem of e-learning systems, learners are
given learning contents that do not match individual aptitudes.
This paper aims to design a rule base for recommendations
focusing on e-learning and learning profiles which are based on
multiple intelligences. Design of the rule base was divided into
four sections as follows. The first section covered a survey of the
variables. Second section was creation of the questionnaire.
Third section was a survey of the student sample groups. The
last section was an analysis of data generated from the results of
the survey. The process of selection for the rule base was
undertaken by comparing the performance of the following
algorithms 1) ID3 algorithm 2) C4.5 algorithm 3) NBTree
algorithm 4) Naïve Bayes algorithm 5) Bayes Net algorithm. The
C4.5 algorithm had the highest percentage of prediction.
Percentage of prediction from the C4.5 algorithm equaled
Index Terms—E-learning, recommendation system, data mining, multiple intelligence.
The authors are with the Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand (e-mail: firstname.lastname@example.org, Nattavee@kmutnb.ac.th, Monchai@kmutnb.ac.th).
Cite: T. Kaewkiriya, N. Utakrit, and M. Tiantong, "The Design of a Rule Base for an e-Learning Recommendation System Base on Multiple Intelligences," International Journal of Information and Education Technology vol. 6, no. 3, pp. 206-210, 2016.