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<p>Augmented Reality (AR) and Game-Based Learning (GBL) are emerging as transformative tools in education, particularly for developing computational thinking (CT) skills among middle school students. This study investigates the integration of AR-based GBL with eye-tracking technology and Epistemic Network Analysis (ENA) to evaluate learning outcomes and cognitive engagement. The study was conducted with 67 middle school students using a pre-test/post-test design. Participants engaged with an AR programming game specifically designed to foster CT skills. They used a marker-based AR educational game and game cards to solve programming challenges across 12 levels. An eye-tracking device recorded gaze fixation patterns, revealing students’ focus areas during gameplay. Quantitative analysis of the eye-tracking data, combined with ENA, highlighted differences between students with higher prior CT skills (HPCT) and lower prior CT skills (LPCT). HPCT students demonstrated strategic engagement, concentrating on advanced game levels and instructional content, whereas LPCT students primarily focused on foundational stages. Qualitative feedback analyzed with ENA from open-ended questionnaires provided additional insights. HPCT students offered constructive, well-justified feedback, indicating a deeper understanding of the educational potential of the AR environment. In contrast, LPCT students’ responses—though generally positive—lacked specificity, reflecting their difficulties with higher-order problem-solving. ENA revealed distinct patterns in how the two groups processed information and articulated feedback, underscoring the need for personalized instructional design. This research contributes a robust methodological framework by leveraging multimodal data to assess AR’s impact on CT development. Integrating quantitative and qualitative data through ENA in AR-GBL contexts can inform the design of more adaptable and inclusive learning environments. Future studies should examine the scalability of these findings across diverse populations and investigate the long-term effects of AR-GBL interventions on skill retention.</p>

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Evaluating computational thinking skills in augmented reality game-based learning (AR-GBL) using eye-tracking and epistemic network analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月29日

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  • Toluchuri Shalini Shanker Rao,
  • Kaushal Kumar Bhagat

摘要

Augmented Reality (AR) and Game-Based Learning (GBL) are emerging as transformative tools in education, particularly for developing computational thinking (CT) skills among middle school students. This study investigates the integration of AR-based GBL with eye-tracking technology and Epistemic Network Analysis (ENA) to evaluate learning outcomes and cognitive engagement. The study was conducted with 67 middle school students using a pre-test/post-test design. Participants engaged with an AR programming game specifically designed to foster CT skills. They used a marker-based AR educational game and game cards to solve programming challenges across 12 levels. An eye-tracking device recorded gaze fixation patterns, revealing students’ focus areas during gameplay. Quantitative analysis of the eye-tracking data, combined with ENA, highlighted differences between students with higher prior CT skills (HPCT) and lower prior CT skills (LPCT). HPCT students demonstrated strategic engagement, concentrating on advanced game levels and instructional content, whereas LPCT students primarily focused on foundational stages. Qualitative feedback analyzed with ENA from open-ended questionnaires provided additional insights. HPCT students offered constructive, well-justified feedback, indicating a deeper understanding of the educational potential of the AR environment. In contrast, LPCT students’ responses—though generally positive—lacked specificity, reflecting their difficulties with higher-order problem-solving. ENA revealed distinct patterns in how the two groups processed information and articulated feedback, underscoring the need for personalized instructional design. This research contributes a robust methodological framework by leveraging multimodal data to assess AR’s impact on CT development. Integrating quantitative and qualitative data through ENA in AR-GBL contexts can inform the design of more adaptable and inclusive learning environments. Future studies should examine the scalability of these findings across diverse populations and investigate the long-term effects of AR-GBL interventions on skill retention.