Build_PC: Mobile Augmented Reality for Supporting PC Hardware Learning With Low Mental Workload

IF 4.9 Q1 PSYCHOLOGY, MULTIDISCIPLINARY
Santiago Criollo-C, Andrea Guerrero-Arias, Javier Guaña-Moya, Soha Rawas, Agariadne Dwinggo Samala, Sergio Luján-Mora
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Abstract

The use of emerging technologies such as mobile augmented reality (MAR) in education has gained relevance, especially in technical areas such as PC hardware learning. However, a persistent problem is the cognitive load that these applications may impose on students, which could affect learning effectiveness and acceptance. This issue is crucial, as a high mental workload can lead to disengagement and lower knowledge retention. Previous studies have explored technological solutions for education but in many cases failed to optimize cognitive load or generate a satisfactory learning environment. This study proposes a MAR application called Build_PC, designed for hands-on teaching of PC hardware. The NASA-TLX tool was used to evaluate the mental workload in 60 students, analyzing dimensions such as mental, physical, temporal, performance, effort, and frustration level. The results show a low mental workload among students, suggesting that Build_PC achieves a balance between task complexity and interface usability. These findings have significant implications: The use of Build_PC in education enables effective, autonomous learning in a technologically advanced environment without imposing excessive mental workload. This supports MAR’s potential as an effective and sustainable learning tool, particularly in contexts where access to physical laboratories is limited.

Abstract Image

Build_PC:移动增强现实支持PC硬件学习与低脑力工作量
在教育中使用移动增强现实(MAR)等新兴技术已经获得了相关性,特别是在PC硬件学习等技术领域。然而,一个持续存在的问题是,这些应用程序可能会给学生带来认知负荷,这可能会影响学习效果和接受度。这个问题至关重要,因为高脑力负荷会导致脱离工作,降低知识留存率。以往的研究已经探索了教育的技术解决方案,但在许多情况下未能优化认知负荷或产生令人满意的学习环境。本研究提出了一个名为Build_PC的MAR应用程序,设计用于PC硬件的动手教学。采用NASA-TLX工具对60名学生的心理负荷进行了评估,分析了心理、身体、时间、表现、努力和沮丧程度等维度。结果显示,学生的心理负荷较低,表明Build_PC在任务复杂性和界面可用性之间取得了平衡。这些发现具有重要意义:在教育中使用Build_PC可以在技术先进的环境中实现有效的自主学习,而不会强加过多的精神负荷。这支持了MAR作为有效和可持续学习工具的潜力,特别是在使用物理实验室的机会有限的情况下。
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来源期刊
Human Behavior and Emerging Technologies
Human Behavior and Emerging Technologies Social Sciences-Social Sciences (all)
CiteScore
17.20
自引率
8.70%
发文量
73
期刊介绍: Human Behavior and Emerging Technologies is an interdisciplinary journal dedicated to publishing high-impact research that enhances understanding of the complex interactions between diverse human behavior and emerging digital technologies.
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