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From Adaptive Learning Support to Fading Out Support for Effective Self-Regulated Online Learning
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Author(s): Yoshiko Goda (Kumamoto University, Japan), Masanori Yamada (Kyushu University, Japan), Takeshi Matsuda (Tokyo Metropolitan University, Japan), Hiroshi Kato (The Open University of Japan, Japan), Yutaka Saito (Fuji Electric Co., Ltd., Japan)and Hiroyuki Miyagawa (Aoyama Gakuin University, Japan)
Copyright: 2023
Pages: 21
Source title:
Research Anthology on Remote Teaching and Learning and the Future of Online Education
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-7540-9.ch014
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Abstract
This chapter applies data mining and learning analytics, along with self-regulated learning (SRL) theories, to examine possible interventions aimed at supporting students' success with online learning. The chapter introduces two learning support systems and the results of related research. These two systems are used as sample cases to describe the relationships among SRL, learning support, learning processes, and learning effects. Case 1 is an early warning system that uses an SRL questionnaire completed before actual learning to determine which students are likely to drop out. Case 2 focuses on student planning and the implementation phases of the SRL cycle. This system supports students' own planning and learning, creating distributed learning and reducing procrastination without human intervention. A comparison of the two cases implies that a combination of an early warning system and system constraints that require planning before actual learning can reduce the need for human learning support and decrease academic procrastination, resulting in increased distributed learning.
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