Use background-characteristic evidence to decide when and for whom to deploy game-based learning and whether to tailor instruction¶

Strategy · All strategies
Evidence · 4 claims (3 for, 1 against) · 2 studies (2 quant-synthesis), q2–q3 · 1 of 2 report an effect size · 4 claims rest on one study

Description¶

The article positions its moderator findings as decision support for educators: knowing whether games work equally for all students should guide "when and for whom to use game-based learning and whether they need to tailor their instruction when using game-based learning in classrooms". Because effects differ by school level and possibly SES, blanket implementation risks "the perpetuation of pre-existing differences between groups". Educators should check which student groups a game intervention serves before adopting it.

Design Implications¶

Context¶

Requirements¶

• Information on students' background characteristics (school level, SES, gender, migration background, SEN) when selecting and implementing game interventions

Constraints¶

• Findings on SES, migration background, and SEN are preliminary and need to be interpreted with caution due to small numbers of studies

Target Learners¶

• Primary and early secondary school students in STEM education

Target Learning Goals¶

• STEM learning outcomes
• STEM motivation

Claims¶

• School level moderates cognitive effects of game-based learning in STEM: games are less effective for secondary than primary school students [+W]
• School level shows a marginal moderating effect on motivational outcomes, with secondary students experiencing a lower motivating effect than primary students [+W]
• Descriptive analyses suggest game-based learning may be less beneficial for cognitive outcomes in low-SES students than high-SES students, but this is preliminary [+W]
• Game design, not learner age or linguistic background, determines DGBL effectiveness; adventure games outperform non-adventure games [-W]

Related Strategies¶

• Culturally tailored chatbot design: position chatbots as reflective companions for Chinese users and as thinking partners for Western users, while deploying cognitive support broadly
• Choose individual texts and videos appropriate for specific students rather than deciding by representational format

Examples¶

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Key Sources¶

• Michaela Arztmann, Lisette Hornstra, Johan Jeuring & Liesbeth Kester (2023). Effects of games in STEM education: a meta-analysis on the moderating role of student background characteristics. Studies in Science Education, 59:1, 109-145. https://doi.org/10.1080/03057267.2022.2057732