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The Research of Applying Big Data to Improving Moocs Learning Analytics:An Empirical Study of College Calculus(1/3)
楊, 鎮華
(PI)
Department of Computer Science and Information Engineering
Overview
Fingerprint
Research output
(2)
Project Details
Status
Finished
Effective start/end date
1/08/17
→
31/07/18
View all
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Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
Students
Engineering & Materials Science
100%
Blended Learning
Social Sciences
69%
Linear regression
Engineering & Materials Science
68%
Principal component analysis
Engineering & Materials Science
63%
Mean square error
Engineering & Materials Science
31%
performance
Social Sciences
31%
Big data
Engineering & Materials Science
23%
learning
Social Sciences
23%
Research output
Research output per year
2018
2018
2018
2
Article
Research output per year
Research output per year
Applying learning analytics for the early prediction of students' academic performance in blended learning
Lu, O. H. T.
,
Huang, A. Y. Q.
,
Huang, J. C. H.
,
Lin, A. J. Q.
,
Ogata, H.
&
Yang, S. J. H.
,
2018
,
In:
Educational Technology and Society.
21
,
2
,
p. 220-232
13 p.
Research output
:
Contribution to journal
›
Article
›
peer-review
Blended Learning
100%
Students
72%
performance
45%
learning
33%
student
24%
161
Scopus citations
Predicting students’ academic performance using multiple linear regression and principal component analysis
Yang, S. J. H.
,
Lu, O. H. T.
,
Huang, A. Y. Q.
,
Huang, J. C. H.
,
Ogata, H.
&
Lin, A. J. Q.
,
Jan 2018
,
In:
Journal of Information Processing.
26
,
p. 170-176
7 p.
Research output
:
Contribution to journal
›
Article
›
peer-review
Open Access
Linear regression
100%
Principal component analysis
93%
Students
73%
Mean square error
45%
Data Analytics
27%
45
Scopus citations