Enhancing the Effectiveness of Industrial IoT Training through the Integration of Augmented Reality and the Quality Function Deployment (QFD) Approach
DOI:
https://doi.org/10.55047/jekombital.v4i3.1087Keywords:
Augmented Reality (AR), Cognitive Load Theory, Experiential Learning, Industrial Internet of Things (IIoT), MQTTAbstract
With the increasing complexity of Industrial Internet of Things (IIoT) systems and the growing demand for effective technical training, innovative learning media are needed to improve learners’ understanding of abstract and dynamic industrial processes. This paper outlines the creation and evaluation of an Augmented Reality (AR) learning approach for better Industrial Internet of Things (IIoT) training delivery. The AR-based medium was developed to address issues of usability associated with the traditional Demo Box experience, including reduced visualization and interaction capacity, and heavy reliance on instructor modeling and explanations. A quantitative survey completed by 110 respondents (44 Demo Box learners and 66 AR media learners) explored the learners' perceptions in four domains: usability, satisfaction, functionality, and learning effectiveness. The results indicate a statistically significant difference after using AR media, in ease of use (4.17 → 5.45) and satisfaction (4.41 → 5.32). The AR process is also capable of collaborating with the IIoT devices using the MQTT protocol, allowing learners to see real-time data being sent and received from the sensors and actuators. In summary, AR-based learning media enhanced learner engagement, reduced cognitive load, and improved conceptual understanding of IIoT processes and acted as an additional educational tool to the Demo Box and discussed how AR media supports IIoT education that leverages immersive, data-driven learning environments.
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