From Adaptive Learning Support to Fading Out Support for Effective Self-Regulated Online Learning

From Adaptive Learning Support to Fading Out Support for Effective Self-Regulated Online Learning

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)
DOI: 10.4018/978-1-7998-5074-8.ch011


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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This section will review previous related literature, after which a summary of the chapter and its scope will be provided. The section includes literature on SRL, online learning behaviors (including academic procrastination), learning supports for online learning, and learning support systems that implement learning predictions and visualizations of the learning process.

Key Terms in this Chapter

E-Mentor: An online supporter of a student’s online learning.

Active Procrastination: Intentionally postponing learning behavior for a specific purpose.

Distributed Learning: Learning is spread throughout the learning period.

Fading Out Learning Support: Decreasing the degree of intervention in student learning.

Academic Procrastination: Postponing learning behavior until shortly before the deadline of tasks and assignments.

Self-Regulated Learning: Learning while managing one’s own cognitive, metacognitive, and affectional resources, behavior, and environment.

Adaptive Learning Support: An intervention to improve student learning that accommodates individual needs.

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