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<p>We examine how gamified platforms shape K–12 learning in authentic classroom workflows in mainland China. Integrating Self-Determination Theory (SDT) and the Technology Acceptance Model (TAM), we surveyed 400 students (Grades 4–9) and 25 teachers. Reflecting the focus on subjective user experience, learning effectiveness was operationalized as perceived learning effectiveness (PLE) rather than objective academic achievement. Using partial least squares structural equation modeling (PLS-SEM), perceived ease of use (PEOU) predicted perceived usefulness (PU), which in turn predicted PLE; this pathway was fully mediated by PU, and learning interest (LI) added a smaller independent effect. Both LI and PLE further predicted behavioral intention (BI) to continue using the platform, and the model explained 53.6% of the variance in PLE (PEOU→PU β = 0.697; PU→PLE β = 0.513; LI→PLE β = 0.291). To address the conceptual proximity of PU and PLE, we refined the PU measure to its TAM-based definition and confirmed discriminant validity using the heterotrait–monotrait ratio (HTMT; all values &lt; 0.85; PU–PLE = 0.803). Teacher data and usage patterns indicated strong in-class activation but weaker after-class support, alongside gaps in teachers’ AI-related teaching knowledge (AI-TPACK). Because PLE is self-reported, we interpret the relationships as predictive associations and offer design and professional-learning implications for converting momentary engagement into sustained learning.</p>

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Interactive gamification in K–12 classrooms: an SDT×TAM mechanism of perceived learning effectiveness in mainland China

  • 期刊论文

  • 发表日期：

    2026年7月22日

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  • Ruochen Ning,
  • Thomas Canhao Xu

摘要

We examine how gamified platforms shape K–12 learning in authentic classroom workflows in mainland China. Integrating Self-Determination Theory (SDT) and the Technology Acceptance Model (TAM), we surveyed 400 students (Grades 4–9) and 25 teachers. Reflecting the focus on subjective user experience, learning effectiveness was operationalized as perceived learning effectiveness (PLE) rather than objective academic achievement. Using partial least squares structural equation modeling (PLS-SEM), perceived ease of use (PEOU) predicted perceived usefulness (PU), which in turn predicted PLE; this pathway was fully mediated by PU, and learning interest (LI) added a smaller independent effect. Both LI and PLE further predicted behavioral intention (BI) to continue using the platform, and the model explained 53.6% of the variance in PLE (PEOU→PU β = 0.697; PU→PLE β = 0.513; LI→PLE β = 0.291). To address the conceptual proximity of PU and PLE, we refined the PU measure to its TAM-based definition and confirmed discriminant validity using the heterotrait–monotrait ratio (HTMT; all values < 0.85; PU–PLE = 0.803). Teacher data and usage patterns indicated strong in-class activation but weaker after-class support, alongside gaps in teachers’ AI-related teaching knowledge (AI-TPACK). Because PLE is self-reported, we interpret the relationships as predictive associations and offer design and professional-learning implications for converting momentary engagement into sustained learning.