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Game-based self-regulated language learning: Theoretical analysis and bibliometrics.

Ruofei Zhang1, Gary Cheng2, Xieling Chen2

  • 1Department of English Language Education, The Education University of Hong Kong, Hong Kong SAR, China.

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Summary
This summary is machine-generated.

Game-based self-regulated language learning (GBSRLL) integrates game-based and self-regulated learning. This new field shows promise, with research focusing on strategies, affective states, and game features.

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Area of Science:

  • Education
  • Computer Science
  • Psychology

Background:

  • Game-based learning and self-regulated learning are established effective methods in language education.
  • Limited research exists on the integration of these two approaches, termed game-based self-regulated language learning (GBSRLL).

Purpose of the Study:

  • To conceptualize game-based self-regulated language learning (GBSRLL).
  • To analyze the emerging interdisciplinary field of GBSRLL by examining existing research.

Main Methods:

  • Theoretical analysis of relevant literature.
  • Thematic evolution analysis of research articles.
  • Social network analysis of publications in game-based language learning and self-regulated language learning.

Main Results:

  • GBSRLL emerged as an interdisciplinary field between 2018-2019.
  • Key research topics include self-regulated learning strategies, affective states, and game features within GBSRLL.
  • The theoretical foundation links learner motivation, self-efficacy, and autonomy with GBSRLL, supported by game mechanics.

Conclusions:

  • GBSRLL is a feasible and promising area for language education.
  • Further research is needed on long-term academic effects and implementation tools for GBSRLL.