Authors:
Kahina Rabahallah
1
;
Latifa Mahdaoui
1
and
Faiçal Azouaou
2
Affiliations:
1
USTHB University, Algeria
;
2
Higher National School of Computer Science and ESI, Algeria
Keyword(s):
MOOCs, Personalized Recommender System, Ontology, Item-based Approach, User-based Approach, Cold-Start Problem.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Cloud Computing
;
Computer-Supported Education
;
e-Learning
;
e-Learning and e-Teaching
;
Enterprise Information Systems
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Semantic Web Technologies
;
Services Science
;
Software Agents and Internet Computing
;
Symbolic Systems
;
User Profiling and Recommender Systems
Abstract:
With Massive Open Online Courses (MOOCs) proliferation, online learners are exposed to various
challenges. Therefore, the lack of personalized recommendation of MOOCs can drive learners to choose
irrelevant MOOCs and then lose their motivation and surrender the learning process. Recommender System
(RS) plays an important role in assisting learners to find appropriate MOOCs to improve learners’
engagements and their satisfaction/completion rates. In this paper, we propose a MOOCs recommender
system combining memory-based Collaborative Filtering (CF) techniques and ontology to recommend
personalized MOOCs to online learners. In our recommendation approach, Ontology is used to provide a
semantic description of learner and MOOC which will be incorporated into the recommendation process to
improve the personalization of learner recommendations whereas CF computes predictions and generates
recommendation. Furthermore, our hybrid approach can relieve the cold-start problem by makin
g use of
ontological knowledge before the initial data to work on are available in the recommender system.
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