A meta-analysis of research on digital game-based science learning

Yu Ling Tsai, Chin Chung Tsai

Research output: Contribution to journalReview article

Abstract

This meta-analysis investigates the relative effectiveness of game-based science learning against other instructional methods (Gameplay design) as well as against science game variants enriched with mechanisms (Game-mechanism design). An overall medium effect size for Gameplay design (k = 14, Nes = 14, gRE = 0.646, p =.000), and an overall small-to-medium effect size for Game-mechanism design (k = 12, Nes = 13, adjusted gRE = 0.270, p =.001) are reported. Further, the results of subgroup analyses suggest that students across educational levels all significantly benefit from game-based science learning although there is no significant difference between the subgroup mean effects. Further, learning and gaming mechanisms play equal roles significantly increasing students' scientific knowledge gains. With these promising results, however, high variance within the subgroups of educational levels and those of gaming mechanisms indicate that gaming mechanisms should be developed with care to meet students' different needs in different educational levels.

Original languageEnglish
Pages (from-to)280-294
Number of pages15
JournalJournal of Computer Assisted Learning
Volume36
Issue number3
DOIs
Publication statusPublished - 2020 Jun 1

Keywords

  • digital game-based science learning
  • educational level
  • game mechanism
  • meta-analysis
  • subgroup analysis

ASJC Scopus subject areas

  • Education
  • Computer Science Applications

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