Learning Analytics of the Relationships among Knowledge Constructions, Self-regulated Learning, and Learning Performance

Hao Hao, Xuewang Geng, Li Chen, Atsushi Shimada, Masanori Yamada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The concept map has a positive effect on the enhancement of self-regulated learning (SRL) and learning performance in terms of cognitive learning tools, according to previous research. However, the relationships between knowledge construction state, learning behaviors, psychological state, and learning performance have not been clearly investigated. Learning analytics (LA) can play an important role in addressing the issue of collecting learning behaviors. This study aims to investigate the relationships between them, using the LA approach. The results indicated that seven knowledge construction types were detected, and knowledge construction type had significant differences in performance, albeit no significant differences in the Tukey post-hoc analyses. Moreover, there is a significant correlation between knowledge map cluster and discussion, some of the factors of SRL (e.g., declarative knowledge, monitoring), and some learning behaviors, such as adding marker, memo, and red marker.

Original languageEnglish
Title of host publicationTALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages290-297
Number of pages8
ISBN (Electronic)9781665436878
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Engineering, Technology and Education, TALE 2021 - Wuhan, China
Duration: Dec 5 2021Dec 8 2021

Publication series

NameTALE 2021 - IEEE International Conference on Engineering, Technology and Education, Proceedings

Conference

Conference2021 IEEE International Conference on Engineering, Technology and Education, TALE 2021
Country/TerritoryChina
CityWuhan
Period12/5/2112/8/21

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Engineering (miscellaneous)
  • Media Technology
  • Education

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