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Study on learning effect prediction models based on principal component analysis in MOOCs

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成果类型:
期刊论文
作者:
Zhang, Wei;Qin, Shiming;Yi, Baolin;Tian, Peng*
通讯作者:
Tian, Peng
作者机构:
[Zhang, Wei; Yi, Baolin; Qin, Shiming] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
[Tian, Peng] Cent China Normal Univ, Coll Publ Adm, Wuhan, Hubei, Peoples R China.
通讯机构:
[Tian, Peng] C
Cent China Normal Univ, Coll Publ Adm, Wuhan, Hubei, Peoples R China.
语种:
英文
关键词:
Predictive models;PCA;Logistic regression algorithm;MOOCs
期刊:
Cluster Computing
ISSN:
1386-7857
年:
2019
卷:
22
期:
6
页码:
15347-15356
机构署名:
本校为第一且通讯机构
院系归属:
公共管理学院
国家数字化学习工程技术研究中心
摘要:
In recent years, the rise and development of massive open online courses (MOOCs) have promoted the boom of online education and also promoted the research on learning analysis and mining based on big data of education. However, while offering a large number of high quality courses, there is also a phenomenon that the overall learning effect is not ideal. How to make effective use of MOOCs for teaching activities poses urgent practical requirements for educators and researchers. MOOCs store massive learners’ learning behavior data, and mining t...

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