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MCRS: A course recommendation system for MOOCs

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成果类型:
期刊论文
作者:
Zhang, Hao;Huang, Tao;Lv, Zhihan*;Liu, SanYa;Zhou, Zhili
通讯作者:
Lv, Zhihan
作者机构:
[Liu, SanYa; Zhang, Hao; Huang, Tao] CCNU, Natl Engn Res Ctr E Learning, Room 419,Sci Hall,152 Luoyu Rd, Wuhan 430072, Hubei, Peoples R China.
[Lv, Zhihan] UCL, Dept Comp Sci, London, England.
[Zhou, Zhili] Nanjing Univ Informat Sci & Technol, Nanjing, Jiangsu, Peoples R China.
通讯机构:
[Lv, Zhihan] U
UCL, Dept Comp Sci, London, England.
语种:
英文
关键词:
MOOC;Online course;Course recommendation;Apriori;Hadoop;Spark;Distributed computation
期刊:
Multimedia Tools and Applications
ISSN:
1380-7501
年:
2018
卷:
77
期:
6
页码:
7051-7069
基金类别:
National Programs for Science and Technology Development [2015BAK07B03]; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD); Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET); CCNU from the colleges' basic research and operation of MOE [CCNU17QN0004]
机构署名:
本校为第一机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
With the popularization development of MOOC platform, the number of online courses grows rapidly. Efficient and appropriate course recommendation can improve learning efficiency. Traditional recommendation system is applied to the closed educational environment in which the quantity of courses and users is relatively stable. Recommendation model and algorithm cannot directly be applied to MOOC platform efficiently. With the light of the characteristics of MOOC platform, MCRS proposed in this paper has made great improvement in the course recommendation model and recommendation algorithm. MCRS ...

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