ULEARN: Personalized Course Learning Objects Based on Hybrid Recommendation Approach

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Date
2018-12Abstract
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.
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.
ISSN : 2010-3689
Research Group : Software Technology Research Laboratory (STRL)
Research Institute : Cyber Technology Institute (CTI)
Peer Reviewed : Yes