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Revealing at-risk learning patterns and corresponding self-regulated strategies via LSTM encoder and time-series clustering

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成果类型:
期刊论文
作者:
Mingyan Zhang;Xu Du;Kerry Rice;Jui-Long Hung;Hao Li
通讯作者:
Hung, J.-L.
作者机构:
[Zhang M.; Li H.; Du X.] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
[Hung J.-L.; Rice K.] Department of Educational Technology, College of Education, Boise State University, Boise, ID, United States
通讯机构:
[Hung, J.-L.] D
Department of Educational Technology, College of Education, Boise State University, Boise, ID, United States
语种:
英文
关键词:
Early warning in online education;Learning pattern analysis;Learning performance prediction;Long short-term memory encoder
期刊:
Information Discovery and Delivery
ISSN:
2398-6247
年:
2022
卷:
50
期:
2
页码:
206-216
基金类别:
This work was supported by the National Natural Science Foundation of China under Grant 61807013 and 61877027.
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:
Purpose: This study aims to propose a learning pattern analysis method which can improve a predictive model’s performance, as well as discover hidden insights into micro-level learning pattern. Analyzing student’s learning patterns can help instructors understand how their course design or activities shape learning behaviors; depict students’ beliefs about learning and their motivation; and predict learning performance by analyzing individual students’ learning patterns. Although time-series analysis is one of the most feasible predictive m...

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