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Student Performance Prediction via Online Learning Behavior Analytics

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成果类型:
会议论文
作者:
Zhang, Wei;Huang, Xujun*;Wang, Shengming;Shu, Jiangbo;Liu, Hai;...
通讯作者:
Huang, Xujun
作者机构:
[Zhang, Wei; Wang, Shengming; Huang, Xujun; Shu, Jiangbo; Chen, Hao; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
通讯机构:
[Huang, Xujun] C
Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
语种:
英文
关键词:
online learning platform;student performance prediction model;learning behavior analytics
期刊:
2017 INTERNATIONAL SYMPOSIUM ON EDUCATIONAL TECHNOLOGY (ISET 2017)
年:
2017
页码:
153-157
会议名称:
International Symposium on Educational Technology (ISET)
会议时间:
JUN 27-29, 2017
会议地点:
City Univ Hong Kong, Hong Kong, HONG KONG
会议主办单位:
City Univ Hong Kong
会议赞助商:
IEEE Hong Kong Sect Comp Chapter, Caritas Inst Higher Educ, Hong Kong Soc Multimedia & Image Comp
主编:
Wang, FL Au, O Ng, KK Shang, J Kwan, R
出版地:
345 E 47TH ST, NEW YORK, NY 10017 USA
出版者:
IEEE
ISBN:
978-1-5090-3031-6
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61505064]; National Natural Science Foundation of Hubei Province [2016CFB497]; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [CCNU17TS0002]; Project of the Program for National Key Technology Research and Development Program [2013BAH72B01, 2013BAH72B05, 2014BAH22F01, 2015BAK07B03, 2015BAH33F02]; CCNU from the colleges' basic research and operation of MOEMinistry of Education, Singapore [CCNU16JYKX031, CCNU16JYKX027]
机构署名:
本校为第一且通讯机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
With the continuous development of online learning platforms, educational data analytics and prediction have become a promising research field, which are helpful for the development of personalized learning system. However, the indicator's selection process does not combine with the whole learning process, which may affect the accuracy of prediction results. In this paper, we induce 19 behavior indicators in the online learning platform, proposing a student performance prediction model which combines with the whole learning process. The model consists of four parts: data collection and pre-pro...

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