The effects of metacognition on online learning interest and continuance to learn with MOOCs

Ya hsun Tsai, Chien hung Lin, Jon chao Hong, Kai hsin Tai

Research output: Contribution to journalArticle

34 Citations (Scopus)

Abstract

Developments in technology have made online teacher training applicable to MOOCs, but the validation of MOOCs presents some challenges, including the high dropout rate and low continuance intention to learn via MOOCs. The purpose of this study is to propose a unified model integrating metacognition and learning interest to investigate continuance intention to learn via MOOCs. Data of 126 respondents were collected and subjected to confirmatory factor analysis. Furthermore, the relationships were tested with structural equation modeling and the results revealed that metacognition was positively related to three levels of learning interest (i.e., liking, enjoyment, and engagement). The three levels of learning interest were positively related to continuance intention to use MOOCs. The findings imply that enhancing learners' metacognition can contribute to increased online learning interest and continuance to learn with MOOCs, thereby reinforcing the benefits of developing teacher training programs via MOOCs.

Original languageEnglish
Pages (from-to)18-29
Number of pages12
JournalComputers and Education
Volume121
DOIs
Publication statusPublished - 2018 Jun

Keywords

  • Continuance intention
  • MOOCs
  • Metacognition
  • Online learning interest

ASJC Scopus subject areas

  • Computer Science(all)
  • Education

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