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A Bayesian Meta-Analysis of Digital Game-Enhanced Vocabulary Learning

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Title: A Bayesian Meta-Analysis of Digital Game-Enhanced Vocabulary Learning
Language: English
Authors: Sofiya Shahiwala (ORCID 0009-0008-1837-6635), D. R. Rahul (ORCID 0000-0002-4215-1769)
Source: Journal of Computer Assisted Learning. 2026 42(3).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 23
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Information Analyses
Descriptors: Bayesian Statistics, Meta Analysis, Game Based Learning, Vocabulary Development, Computer Games, Instructional Effectiveness
DOI: 10.1002/jcal.70246
ISSN: 0266-4909
1365-2729
Abstract: Background: Digital game-enhanced vocabulary learning (DGEVL), referring to the use of commercial off-the-shelf games for vocabulary learning, has attracted growing scholarly interest. Although existing studies predominantly report positive effects, previous reviews have conflated different game types, restricted their scope or overlooked the processes that mediate learning outcomes. As a result, there is limited understanding of how and under what conditions DGEVL works, leaving gaps in theory and research practice. Objectives: This study aimed to examine the effectiveness of digital game-enhanced vocabulary learning and identify the factors that facilitate or hinder its impact. Methods: A meta-analysis of 12 studies (14 samples, 765 participants) was conducted using a Bayesian random-effects model to assess the overall impact of DGEVL. Subgroup analyses explored the influence of study, participant, intervention and assessment characteristics. To contextualise these findings, a systematic review of 25 studies, comprising 12 quantitative studies included in the meta-analysis and an additional 13 qualitative studies, was carried out, and a thematic analysis was conducted to construct a conceptual model of the learning process. Results and Conclusions: DGEVL demonstrates a strong positive effect on vocabulary learning (posterior median g[subscript within] = 1.11, 95% credible interval (CI) [0.62, 1.61]; g[subscript between] = 1.40, 95% CI [0.58, 2.25]), with substantial between-study heterogeneity (τ[subscript within] = 0.56, 95% CI [0.26, 1.00]; τ[subscript between] = 0.80, 95% CI [0.26, 1.68]). These effects vary depending on factors such as the length of the intervention, learner proficiency, and the type of game played. The cyclical model conceptualises vocabulary learning as a cyclical process shaped by gameplay, language interactions and metacognitive regulation. The study highlights the need for balanced assessment techniques, prolonged interventions, micro-longitudinal data collection, comparative designs and rigorous trials.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1506850
Database: ERIC
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  Data: A Bayesian Meta-Analysis of Digital Game-Enhanced Vocabulary Learning
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Sofiya+Shahiwala%22">Sofiya Shahiwala</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0008-1837-6635">0009-0008-1837-6635</externalLink>)<br /><searchLink fieldCode="AR" term="%22D%2E+R%2E+Rahul%22">D. R. Rahul</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4215-1769">0000-0002-4215-1769</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Computer+Assisted+Learning%22"><i>Journal of Computer Assisted Learning</i></searchLink>. 2026 42(3).
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: Y
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  Data: 23
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  Data: 2026
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  Data: Journal Articles<br />Reports - Research<br />Information Analyses
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  Data: <searchLink fieldCode="DE" term="%22Bayesian+Statistics%22">Bayesian Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Meta+Analysis%22">Meta Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Game+Based+Learning%22">Game Based Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Vocabulary+Development%22">Vocabulary Development</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Games%22">Computer Games</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Effectiveness%22">Instructional Effectiveness</searchLink>
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  Data: 10.1002/jcal.70246
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  Data: 0266-4909<br />1365-2729
– Name: Abstract
  Label: Abstract
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  Data: Background: Digital game-enhanced vocabulary learning (DGEVL), referring to the use of commercial off-the-shelf games for vocabulary learning, has attracted growing scholarly interest. Although existing studies predominantly report positive effects, previous reviews have conflated different game types, restricted their scope or overlooked the processes that mediate learning outcomes. As a result, there is limited understanding of how and under what conditions DGEVL works, leaving gaps in theory and research practice. Objectives: This study aimed to examine the effectiveness of digital game-enhanced vocabulary learning and identify the factors that facilitate or hinder its impact. Methods: A meta-analysis of 12 studies (14 samples, 765 participants) was conducted using a Bayesian random-effects model to assess the overall impact of DGEVL. Subgroup analyses explored the influence of study, participant, intervention and assessment characteristics. To contextualise these findings, a systematic review of 25 studies, comprising 12 quantitative studies included in the meta-analysis and an additional 13 qualitative studies, was carried out, and a thematic analysis was conducted to construct a conceptual model of the learning process. Results and Conclusions: DGEVL demonstrates a strong positive effect on vocabulary learning (posterior median g[subscript within] = 1.11, 95% credible interval (CI) [0.62, 1.61]; g[subscript between] = 1.40, 95% CI [0.58, 2.25]), with substantial between-study heterogeneity (τ[subscript within] = 0.56, 95% CI [0.26, 1.00]; τ[subscript between] = 0.80, 95% CI [0.26, 1.68]). These effects vary depending on factors such as the length of the intervention, learner proficiency, and the type of game played. The cyclical model conceptualises vocabulary learning as a cyclical process shaped by gameplay, language interactions and metacognitive regulation. The study highlights the need for balanced assessment techniques, prolonged interventions, micro-longitudinal data collection, comparative designs and rigorous trials.
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      – SubjectFull: Bayesian Statistics
        Type: general
      – SubjectFull: Meta Analysis
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      – SubjectFull: Game Based Learning
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      – SubjectFull: Vocabulary Development
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      – SubjectFull: Computer Games
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