Open AccessPersonalization of E-Learning Systems: Determination of Most Preferred Learning Style Using Conjoint Analysis
Author(s) -
J. Saul Nicholas,
F. Sagayaraj Francis
Publication year - 2018
Publication title -
asian journal of computer science and technology
Language(s) - English
Resource type - Journals
eISSN - 2583-7907
pISSN - 2249-0701
DOI - 10.51983/ajcst-2018.7.3.1889
Subject(s) - preference learning , personalization , ranking (information retrieval) , style (visual arts) , conjoint analysis , computer science , preference , product (mathematics) , learning styles , human–computer interaction , psychology , artificial intelligence , world wide web , mathematics education , mathematics , statistics , geometry , archaeology , history
Identifying user preferences is a very important activity before offering a suggestion or a product. E-learning systems also follow suit in identifying the user preferences of learning style before offering the e-learning contents. There are several methods discussed in the literature for identifying the user preferences for e-learning contents. This paper presents a new method for the same purpose. The core of the new method is Conjoint Analysis, which is based on the type of the contents, preferred volume for each type of content and the ranking for the various combinations of the contents and their preferred volumes. The outcome of this method is the most preferred learning style of an individual learner.