ULEARN: Personalized Course Learning Objects Based on Hybrid Recommendation Approach

Date

2018-12

Advisors

Journal Title

Journal ISSN

ISSN

2010-3689

Volume Title

Publisher

International Journal of Information and Education Technology

Type

Article

Peer reviewed

Yes

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.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Course content, recommender system, learning object, learner profile, teaching strategy

Citation

Nafea, S., Siewe, F., He, Y. (2018) ULEARN: Personalized Course Learning Objects Based on Hybrid Recommendation Approach. International Journal of Information and Education Technology, 8(12), pp.842-847.

Rights

Research Institute

Cyber Technology Institute (CTI)