• May 14, 2019 News!IJIET Vol. 7, No. 9-Vol. 8, No. 8 have been indexed by EI (Inspec).   [Click]
  • Apr 15, 2019 News![Call for Papers] Special Issue on Education, Research and Innovation   [Click]
  • Sep 06, 2019 News!Vol. 9, No. 10 issue has been published online!   [Click]
General Information
    • ISSN: 2010-3689 (Online)
    • Abbreviated Title: Int. J. Inf. Educ. Technol.
    • Frequency: Monthly
    • DOI: 10.18178/IJIET
    • Editor-in-Chief: Prof. Dr. Steve Thatcher
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: Scopus (Since 2019), EI(INSPEC, IET), EBSCO, Electronic Journals Library, Google Scholar, Crossref, etc.
    • E-mail: ijiet@ejournal.net
Prof. Dr. Steve Thatcher
CQUniversity, Australia
It is my honor to be the editor-in-chief of IJIET. The journal publishes good-quality papers which focous on the advanced researches in the field of information and education technology. Hopefully, IJIET will become a recognized journal among the scholars in the related fields.

IJIET 2018 Vol.8(12): 842-847 ISSN: 2010-3689
doi: 10.18178/ijiet.2018.8.12.1151

ULEARN: Personalized Course Learning Objects Based on Hybrid Recommendation Approach

Shaimaa M. Nafea, Francois Siewe, and Ying He
Abstract—The success of e-learning systems depends on their capability to automatically retrieve and recommend relevant learning content according to the preferences of specific learner profiles. Generally, e-learning systems do not cater for individual learners’ needs based on their profile. They also make it very difficult for learners to choose suitable resources for their learning. Matching the teaching strategy with the most appropriate learning object based on learning styles is presented in this paper, with the aim of improving learners’ academic levels. This work focuses on the design of a personalized e-learning environment based on a hybrid recommender system, collaborative filtering and item content filtering. It also describes the architecture of the ULEARN system. The ULEARN uses a recommender adaptive teaching strategy by choosing and sequencing learning objects that fit with the learners’ learning styles. The proposed system can be used to rearrange learning object priority to match the student’s adaptive profile and to adapt teaching strategy, in order to improve the quality of learning.

Index Terms—Course content, recommender system, learning object, learner profile, teaching strategy.

Shaimaa M. Nafea is with the School of Business (Management Information System) Arab Academy for Science Technology & Maritime, Cairo, Egypt (e-mail: P15017421@myemail.dmu.ac.uk).
Francois Siewe and Ying He are with the School of Computer Science and Informatics, De Montfort University, Leicester, UK (e-mail: fsiewe@dmu.ac.uk, ying.he@dmu.ac.uk).


Cite: Shaimaa M. Nafea, Francois Siewe, and Ying He, "ULEARN: Personalized Course Learning Objects Based on Hybrid Recommendation Approach," International Journal of Information and Education Technology vol. 8, no. 12, pp. 842-847, 2018.

Copyright © 2008-2019. International Journal of Information and Education Technology. All rights reserved.
E-mail: ijiet@ejournal.net