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<p>Augmented Reality (AR) has shown potential to support STEM education by connecting abstract concepts with hands-on practice. However, existing research on AR for learning electronics, particularly Arduino platforms, often lacks comparisons with other interactive digital approaches and tends to rely on simulated rather than real physical components. To address these gaps, this study proposes a progressive AR-based learning framework that integrates real Arduino hardware with three sequential learning modes (Exploration, Practice, and Assembly), grounded in scaffolding, experiential learning, and cognitive load theories. A comparative experimental study was conducted with 47 participants assigned to either an AR-based condition or an equivalent web-based interactive condition. Pre- and post-tests measured knowledge gains in component identification, structural understanding, and basic procedural circuit knowledge. The AR group showed statistically significant within-group improvement (<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(t = -3.603\)</EquationSource></InlineEquation>, <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(p = 0.0015\)</EquationSource></InlineEquation>, <InlineEquation ID="IEq3"><EquationSource Format="TEX">\(d = 0.735\)</EquationSource></InlineEquation>), while the web-based group showed marginal improvement that did not reach significance (<InlineEquation ID="IEq4"><EquationSource Format="TEX">\(p = 0.0512\)</EquationSource></InlineEquation>, <InlineEquation ID="IEq5"><EquationSource Format="TEX">\(d = 0.430\)</EquationSource></InlineEquation>). Critically, direct comparison of gain scores between groups revealed no significant difference (<InlineEquation ID="IEq6"><EquationSource Format="TEX">\(p = 0.4083\)</EquationSource></InlineEquation>, Cohen’s <InlineEquation ID="IEq7"><EquationSource Format="TEX">\(d = 0.244\)</EquationSource></InlineEquation>). Thus, although AR produced significant learning gains within the group, it did not statistically outperform the web-based interactive alternative. User perceptions (SAMR-based questionnaire, <InlineEquation ID="IEq8"><EquationSource Format="TEX">\(n = 17\)</EquationSource></InlineEquation>) were highly positive regarding utility and motivation (94.1% agreement), although neutral responses on items related to deeper conceptual understanding suggest that perceived benefits were stronger for engagement, motivation, and visualization than for higher-order conceptual learning. Qualitative feedback indicated that participants perceived the system as helpful for visualizing components and accessing component information more intuitively. Overall, the findings suggest that AR can serve as a valuable complementary tool in electronics education when embedded in a theory-informed instructional design, although the present study does not provide evidence of superior learning effectiveness compared with a well-designed interactive web-based alternative.</p>

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A progressive augmented reality framework for learning Arduino through physical and digital interaction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月3日

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  • Anna Tonda,
  • Linda García Rytman,
  • Águeda Gómez-Cambronero,
  • Inmaculada Remolar

摘要

Augmented Reality (AR) has shown potential to support STEM education by connecting abstract concepts with hands-on practice. However, existing research on AR for learning electronics, particularly Arduino platforms, often lacks comparisons with other interactive digital approaches and tends to rely on simulated rather than real physical components. To address these gaps, this study proposes a progressive AR-based learning framework that integrates real Arduino hardware with three sequential learning modes (Exploration, Practice, and Assembly), grounded in scaffolding, experiential learning, and cognitive load theories. A comparative experimental study was conducted with 47 participants assigned to either an AR-based condition or an equivalent web-based interactive condition. Pre- and post-tests measured knowledge gains in component identification, structural understanding, and basic procedural circuit knowledge. The AR group showed statistically significant within-group improvement (\(t = -3.603\), \(p = 0.0015\), \(d = 0.735\)), while the web-based group showed marginal improvement that did not reach significance (\(p = 0.0512\), \(d = 0.430\)). Critically, direct comparison of gain scores between groups revealed no significant difference (\(p = 0.4083\), Cohen’s \(d = 0.244\)). Thus, although AR produced significant learning gains within the group, it did not statistically outperform the web-based interactive alternative. User perceptions (SAMR-based questionnaire, \(n = 17\)) were highly positive regarding utility and motivation (94.1% agreement), although neutral responses on items related to deeper conceptual understanding suggest that perceived benefits were stronger for engagement, motivation, and visualization than for higher-order conceptual learning. Qualitative feedback indicated that participants perceived the system as helpful for visualizing components and accessing component information more intuitively. Overall, the findings suggest that AR can serve as a valuable complementary tool in electronics education when embedded in a theory-informed instructional design, although the present study does not provide evidence of superior learning effectiveness compared with a well-designed interactive web-based alternative.