Analysis of Differences in Self-Regulated Learning Behavior Patterns of Online Learners
Self-regulated learning is one of the important skills to achieve learning goals and is also the key factor to ensure the quality of online learning. With the rapid development of intelligent education and information technology, online learning behavior has become a new trend in the development of...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/23/4013 |
| _version_ | 1797463376389996544 |
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| author | Zi Ye Lei Jiang Yang Li Zhaoting Wang Guodao Zhang Huiling Chen |
| author_facet | Zi Ye Lei Jiang Yang Li Zhaoting Wang Guodao Zhang Huiling Chen |
| author_sort | Zi Ye |
| collection | DOAJ |
| description | Self-regulated learning is one of the important skills to achieve learning goals and is also the key factor to ensure the quality of online learning. With the rapid development of intelligent education and information technology, online learning behavior has become a new trend in the development of education modernization. Behavior data of online learning platforms are an important carrier to reflect the learners’ initiative to plan, monitor, and regulate their learning process. Self-regulated learning (SRL) is one of the important skills to achieve learning goals and is an essential means to ensure the quality of online learning. However, there are still great challenges in studying the types and sequential patterns of learners’ self-regulated learning behaviors in online environments. In addition, for higher education, the defects of the traditional education mode are increasingly prominent, and self-regulated learning (SRL) has become an inevitable trend. Based on Zimmerman’s self-regulation theory model, this paper first classifies learning groups using the hierarchical clustering method. Then, lag sequence analysis is used to explore the most significant differences in SRL behavior and its sequence patterns among different learning groups. Finally, the differences in academic achievement among different groups are discussed. The results are as follows: (1) The group with more average behavior frequency tends to solve online tasks actively, presenting a “cognitive oriented” sequential pattern, and this group has the best performance; (2) the group with more active behavior frequency tends to improve in the process of trial and error, showing a “reflective oriented” sequence pattern, and this group has better performance; (3) the group with the lowest behavior frequency tends to passively complete the learning task, showing a “negative regulated” sequence pattern, and this group has poor performance. From the aspects of stage and outcome of self-regulated learning, the behavior sequence and learning performance of online learning behavior mode are compared, and the learning path and learning performance of different learning modes are fully analyzed, which can provide reference for the improvement of online learning platform and teachers’ teaching intervention. |
| first_indexed | 2024-03-09T17:49:47Z |
| format | Article |
| id | doaj.art-c229cf236f324b7982c7a1bb788c37c5 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T17:49:47Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-c229cf236f324b7982c7a1bb788c37c52023-11-24T10:49:14ZengMDPI AGElectronics2079-92922022-12-011123401310.3390/electronics11234013Analysis of Differences in Self-Regulated Learning Behavior Patterns of Online LearnersZi Ye0Lei Jiang1Yang Li2Zhaoting Wang3Guodao Zhang4Huiling Chen5School of Marxism, Zhejiang Institute of Economics and Trade, Hangzhou 310018, ChinaTechnology Center, Hangzhou Oxygen Plant Group Co., Ltd., Hangzhou 310004, ChinaSchool of Marxism, Zhejiang Institute of Economics and Trade, Hangzhou 310018, ChinaSchool of Marxism, Zhejiang Institute of Economics and Trade, Hangzhou 310018, ChinaSchool of Media and Design, Hangzhou Dianzi University, Hangzhou 310018, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, ChinaSelf-regulated learning is one of the important skills to achieve learning goals and is also the key factor to ensure the quality of online learning. With the rapid development of intelligent education and information technology, online learning behavior has become a new trend in the development of education modernization. Behavior data of online learning platforms are an important carrier to reflect the learners’ initiative to plan, monitor, and regulate their learning process. Self-regulated learning (SRL) is one of the important skills to achieve learning goals and is an essential means to ensure the quality of online learning. However, there are still great challenges in studying the types and sequential patterns of learners’ self-regulated learning behaviors in online environments. In addition, for higher education, the defects of the traditional education mode are increasingly prominent, and self-regulated learning (SRL) has become an inevitable trend. Based on Zimmerman’s self-regulation theory model, this paper first classifies learning groups using the hierarchical clustering method. Then, lag sequence analysis is used to explore the most significant differences in SRL behavior and its sequence patterns among different learning groups. Finally, the differences in academic achievement among different groups are discussed. The results are as follows: (1) The group with more average behavior frequency tends to solve online tasks actively, presenting a “cognitive oriented” sequential pattern, and this group has the best performance; (2) the group with more active behavior frequency tends to improve in the process of trial and error, showing a “reflective oriented” sequence pattern, and this group has better performance; (3) the group with the lowest behavior frequency tends to passively complete the learning task, showing a “negative regulated” sequence pattern, and this group has poor performance. From the aspects of stage and outcome of self-regulated learning, the behavior sequence and learning performance of online learning behavior mode are compared, and the learning path and learning performance of different learning modes are fully analyzed, which can provide reference for the improvement of online learning platform and teachers’ teaching intervention.https://www.mdpi.com/2079-9292/11/23/4013self-regulated learningintelligent educationmachine learninghierarchical clusteringbehavior sequence |
| spellingShingle | Zi Ye Lei Jiang Yang Li Zhaoting Wang Guodao Zhang Huiling Chen Analysis of Differences in Self-Regulated Learning Behavior Patterns of Online Learners Electronics self-regulated learning intelligent education machine learning hierarchical clustering behavior sequence |
| title | Analysis of Differences in Self-Regulated Learning Behavior Patterns of Online Learners |
| title_full | Analysis of Differences in Self-Regulated Learning Behavior Patterns of Online Learners |
| title_fullStr | Analysis of Differences in Self-Regulated Learning Behavior Patterns of Online Learners |
| title_full_unstemmed | Analysis of Differences in Self-Regulated Learning Behavior Patterns of Online Learners |
| title_short | Analysis of Differences in Self-Regulated Learning Behavior Patterns of Online Learners |
| title_sort | analysis of differences in self regulated learning behavior patterns of online learners |
| topic | self-regulated learning intelligent education machine learning hierarchical clustering behavior sequence |
| url | https://www.mdpi.com/2079-9292/11/23/4013 |
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