From adaptive learning support to fading out support for effective self-regulated online learning

Yoshiko Goda, Masanori Yamada, Takeshi Matsuda, Hiroshi Kato, Yutaka Saito, Hiroyuki Miyagawa

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationEarly Warning Systems and Targeted Interventions for Student Success in Online Courses
PublisherIGI Global
Pages218-238
Number of pages21
ISBN (Electronic)9781799850755
ISBN (Print)9781799850748
DOIs
Publication statusPublished - Jun 26 2020

All Science Journal Classification (ASJC) codes

  • General Social Sciences

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