Enhancing Computational Thinking Skills of the Future Construction Workforce to Perform Sensor Data Analytics with End-User Programming Environment

Abstract

Despite being one of the most significant employment hubs of the United States, the construction industry continues to struggle with declining productivity, workplace hazards, and a growing shortage of skilled workers. In response to these challenges, the construction industry is leveraging advanced sensing technologies and data-driven analytical methods that can significantly improve information accessibility, enabling quicker and more informed decision-making. Accordingly, the expanding range of sensor-based applications triggers a high demand to equip the future workforce with specialized skills. However, for many construction engineering and management graduates, the expanding complexity of data and technology creates comprehension difficulties regarding essential computational concepts and procedural workflows. This ultimately restricts the workforce's ability to convert data into actionable insights for informed decision-making. To bridge similar skill gaps, end-user programming offers considerable potential for learners to execute analytical operations on sensor data through visual programming mechanics. Such affordances also foster computational thinking skills, which are essential for transforming unstructured sensor data into actionable intelligence. This research explores how block-based programming environments can be integrated into construction engineering and management education to improve technical skills, particularly in sensor data analytics. Using a mixed-method approach, the research first surveyed industry professionals to identify the required competencies. Based on this input, a block-based programming environment was designed to help students learn data analytics using authentic sensor data. The environment's efficiency and effectiveness were evaluated with construction students, focusing on key factors such as usability, cognitive load, and visual attention demand. The environment was implemented in classrooms for a summative assessment to measure learners' self-efficacy in targeted skills, performance, and technology acceptance. Additionally, the assessment examined demographic factors impacting user interaction. The affordances and effectiveness of block-based environments contribute to the Learning-for-Use framework by utilizing graphical, interactive programming elements to develop procedural knowledge for solving sensor data analytics problems. The findings present significant insights into the interaction dynamics and perceptions associated with the contextual utilization of block-based programming environments. Furthermore, this research contributes to the dissemination of pedagogical resources that can advance the application and use of emerging technology and computing within the construction sector.