InerSens: A Block-Based Programming Platform for Learning Sensor Data Analytics in Construction Engineering Programs

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.