Analyzing and Interpreting Students’ Self-regulated Learning Patterns Combining Time-series Feature Extraction, Segmentation, and Clustering

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成果类型：

期刊论文

作者：

Zhang, Mingyan;Du, Xu;Hung, Jui-Long;Li, Hao;Liu, Mengfan;...

通讯作者：

Jui-Long Hung

作者机构：

[Zhang, Mingyan] Zhejiang Normal Univ, Coll Teacher Educ, Jinhua, Zhejiang, Peoples R China.

[Li, Hao; Du, Xu; Liu, Mengfan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.

[Hung, Jui-Long] Boise State Univ, Dept Educ Technol, 1910 Univ Dr, Boise, ID 83725 USA.

[Hung, Jui-Long] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.

[Tang, Hengtao] Univ South Carolina, Dept Educ Studies, Columbia, SC USA.

通讯机构：

[Jui-Long Hung] D

Department of Educational Technology, Boise State University, Boise, ID, USA<&wdkj&>National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, China

语种：

英文

关键词：

Long Short-Term Memory autoencoder;learning pattern;time-series turning points;learning performance

期刊：

Journal of Educational Computing Research

ISSN：

0735-6331

年：

2022

卷：

期：

页码：

1130 - 1165

DOI：

10.1177/07356331211065097

基金类别：

National Natural Science Foundation of China [61807013, 61877027]

机构署名：

本校为其他机构

院系归属：

国家数字化学习工程技术研究中心

摘要：

In online learning, students' learning behavior might change as the course progresses. How students adjust learning behaviors aligned with course requirements reflects their self-regulated learning strategies. Analyzing students' learning patterns can help instructors understand how the course design or activities shape students' learning behaviors, including their learning beliefs and motivation, and facilitate teaching decision makings accordingly. This study aims to propose a scientific analytic method to understand students' self-regulated learning (SRL) patterns. The whole process include...

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