The Effect of the Integration of Metacognitive and Motivation Scaffolding Through a Pedagogical Agent on Self- and Co- Regulation Learning

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Abstract

Nowadays, online learning has become commonplace in higher education. Various factors influence the success of online learning. Factors such as low self-regulation and co-regulation learning skills can affect student engagement and motivation in online learning activities. Therefore, it is essential to provide external support in the online learning process. Pedagogical agents are one solution to increasing self-regulation and co-regulation learning in online learning environments. This study aims to determine the effect of integrating metacognitive and motivation scaffolding support provided through pedagogical agents on self-regulation and co-regulation learning skills. This study uses a mixed-methods explanatory sequential approach. A quasi-experimental method of the non-equivalent type (pretest and posttest) control group is used for quantitative data collection, and an in-depth survey is used to analyze qualitative data. The results showed that the learning support provided by pedagogical agents who act as scaffolders using metacognitive and motivation scaffolding integration has a significant impact on increasing students' self-regulation and co-regulation learning skills. Also, the results of qualitative data analysis supported the findings obtained from quantitative data. Research limitations and suggestions for further research are presented at the end of this article.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Learning Technologies
Volume16
Issue number4
DOIs
Publication statusAccepted/In press - 2023

Keywords

  • co-regulated learning
  • Collaborative learning
  • Computers
  • Education
  • Federated learning
  • higher education
  • individual learning
  • metacognitive scaffolding
  • Monitoring
  • motivation scaffolding
  • online-learning
  • pedagogical agent
  • Reflection
  • Regulation
  • self-regulated learning
  • Task analysis

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