| dc.description.abstract | Construction firms face challenges in sourcing qualified candidates for enhancing project outcomes through sensor data analytics. There are limited tools for teaching students from construction-related disciplines how to analyze sensor data. By harnessing the potential of block-based programming, this study designed a pedagogical tool, InerSens, to support construction engineering students with no prior programming experience to analyze sensor data and address real-world construction challenges, such as ergonomic risks. Altogether 20 students participated in an experiment comparing InerSens and a traditional platform, Microsoft Excel, for data analytics. Evaluations involved usability, perceived workload, visual attention, verbal feedback using the System Usability Scale, NASA TLX, eye-tracking metrics, and interviews. InerSens was rated as 8.89% more user-friendly than the traditional tool, with a significantly reduced perceived cognitive load by 46.11%, and a more balanced distribution of visual attention during data analytics tasks. Through the evaluation of cognitive and usability factors, this paper extends the applications of the Learning-for-Use and the Cognitive Load theories, emphasizing their applicability in instructional design, revealing learner needs, and the potential to advance the development of pedagogical tools for data analytics. | |
