DocumentCode :
668860
Title :
The application of data mining technology in the college English network self-learning monitoring system
Author :
Li Xue ; Guo Aidong
Author_Institution :
Foreign Language Inst., Harbin Univ. of Sci. & Technol., Harbin, China
fYear :
2013
fDate :
20-22 Nov. 2013
Firstpage :
666
Lastpage :
668
Abstract :
At first the author lists some research findings concerning learners´ self-efficacy both at home and abroad. Then the college English learning efficacy questionnaire is designed by combining the on-line learning behavior with self-efficacy scales put forward by some linguistics. In order to analyze learners´ online learning statically, the author suggests that the network learning behavior acquisition module should be embedded in the network learning system to analyze factors affecting students´ network self-learning, furthermore, to provide the scientific materials for the college English teaching reform. At last with the data mining technology being the data analysis method, it explores the relationship among students´ self-efficacy, network learning behavior and academic scores in the overall college English network self-learning process involving listening, speaking, reading and writing.
Keywords :
computer aided instruction; data mining; educational institutions; linguistics; natural languages; college english network self-learning monitoring system; college english teaching reform; data analysis method; data mining technology; network learning behavior acquisition module; on-line learning behavior; Algorithm design and analysis; Classification algorithms; Data mining; Data models; Decision trees; Educational institutions; Learning systems; data mining; learning behavior acquisition; network self-Learning; self-efficacy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Consumer Electronics, Communications and Networks (CECNet), 2013 3rd International Conference on
Conference_Location :
Xianning
Print_ISBN :
978-1-4799-2859-0
Type :
conf
DOI :
10.1109/CECNet.2013.6703418
Filename :
6703418
Link To Document :
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