Proceedings of The 2017 International Conference on Advanced Technologies Enhancing Education (ICAT2E 2017)

MCRS: A Course Recommendation System for MOOCs

Authors
Tao Huang, Gaoqiang Zhan, Hao Zhang, Heng Yang
Corresponding Author
Tao Huang
Available Online March 2017.
DOI
10.2991/icat2e-17.2016.20How to use a DOI?
Keywords
MOOC, online course, big data, course recommendation, Apriori, Hadoop, Spark
Abstract

With the popularization of MOOC platform, there is a tendency of big data in the number of online courses. Efficient and appropriate course recommendation can improve learning efficiency. According to the characteristics of MOOC platform, MCRS has made great improvement in course recommendation model and algorithm in this paper. The experimental results proves that MCRS's recommendation algorithm is more efficient than Hadoop Apriori algorithm, and recommend appropriate course to user. It turns out that MCRS is more suitable for MOOC platform.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of The 2017 International Conference on Advanced Technologies Enhancing Education (ICAT2E 2017)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
March 2017
ISBN
10.2991/icat2e-17.2016.20
ISSN
2352-5398
DOI
10.2991/icat2e-17.2016.20How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Tao Huang
AU  - Gaoqiang Zhan
AU  - Hao Zhang
AU  - Heng Yang
PY  - 2017/03
DA  - 2017/03
TI  - MCRS: A Course Recommendation System for MOOCs
BT  - Proceedings of The 2017 International Conference on Advanced Technologies Enhancing Education (ICAT2E 2017)
PB  - Atlantis Press
SP  - 82
EP  - 85
SN  - 2352-5398
UR  - https://doi.org/10.2991/icat2e-17.2016.20
DO  - 10.2991/icat2e-17.2016.20
ID  - Huang2017/03
ER  -