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A meta‐analysis of research on digital game‐based science learning
Author(s) -
Tsai YuLing,
Tsai ChinChung
Publication year - 2020
Publication title -
journal of computer assisted learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.583
H-Index - 93
eISSN - 1365-2729
pISSN - 0266-4909
DOI - 10.1111/jcal.12430
Subject(s) - game based learning , meta analysis , science learning , mechanism (biology) , science education , significant difference , mathematics education , psychology , instructional design , game theory , educational game , digital learning , computer science , game design , multimedia , mathematics , statistics , medicine , philosophy , mathematical economics , epistemology
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, N es = 14, g RE = 0.646, p = .000), and an overall small‐to‐medium effect size for Game‐mechanism design ( k = 12, N es = 13, adjusted g RE = 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.