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Classification of construction resource states, using sensor data analytics, has implications for improving informed decision-making for safety and productivity. However, training on sensor data analytics in construction education faces challenges owing to the complexity of analytical processes and the large stream of raw data involved. This research presents the development and user evaluation of ActionSens, a block-based end-user programming platform, for training students from construction-related disciplines to classify resources using sensor data analytics. ActionSens was designed for construction students to perform sensor data analytics such as activity recognition in construction. ActionSens was compared to traditional tools (i.e., combining Excel and MATLAB) used for performing sensor data analytics in terms of usability, workload, visual attention, and processing time using the System Usability Scale, NASA Task Load Index, eye-tracking, and qualitative feedback. Twenty students participated, performing data analytics tasks with both approaches. ActionSens exhibited a better user experience compared to conventional platforms, through higher usability scores and lower cognitive workload. This was evident through participants' interaction behavior, showcasing optimized attentional resource allocation across key tasks. The study contributes to knowledge by illustrating how the integration of construction domain information into block-based programming environments can equip students with the necessary skills for sensor data analytics. The development of ActionSens contributes to the Learning-for-Use framework by employing graphical and interactive programming objects to foster procedural knowledge for addressing challenges in sensor data analytics. The formative evaluation provides insights into how students engage with the programming environment and assesses the impact of the environment on their cognitive load. 

The construction industry's shift to data-driven project management has led to the increasing adoption of various sensing technologies. The transition triggers a demand for a workforce skilled in both the technical and analytical aspects of these tools. While sensing technologies and data analytics can support construction processes, the inherent complexity of sensor data processing often exceeds the skill sets of the graduating workforce. Further, integrating sensor-based applications into construction curricula lacks evidence to support effectiveness in training future professionals. Computational thinking-supported data practices can allow construction students to perform sensor data analytics, spanning from data generation to visualization. This pilot study utilizes InerSens, a block programming interface, as a pedagogical tool to develop construction students’ computational thinking through sensor-based ergonomic risk assessment. Twenty-six undergraduate students were engaged in instructional units using wearable sensors, data, and InerSens. The effectiveness of the approach was evaluated by examining students' perceived self-efficacy in sensor data analytics skills, task performance and reflections, and technology acceptance. Results show gains in self-efficacy, positive technology acceptance, and satisfactory performance on course assignments. The study contributes to the Learning-for-Use, constructivism, and constructionism frameworks by integrating computational thinking into graphical and interactive programming objects to develop procedural knowledge and by summatively assessing how construction students learn to address challenges with sensor data analytics. 

Data analytics and computational thinking are essential for processing and analyzing data from sensors, and presenting the results in formats suitable for decision-making. However, most undergraduate construction engineering and management students struggle with understanding the required computational concepts and workflows because they lack the theoretical foundations. This has resulted in a shortage of skilled workforce equipped with the required competencies for developing sustainable solutions with sensor data. End-user programming environments present students with a means to execute complex analysis by employing visual programming mechanics. With end-user programming, students can easily formulate problems, logically organize, analyze sensor data, represent data through abstractions, and adapt the results to a wide variety of problems. This paper presents a conceptual system based on end-user programming and grounded in the Learning-for-Use theory which can equip construction engineering and management students with the competencies needed to implement sensor data analytics in the construction industry. The system allows students to specify algorithms by directly interacting with data and objects to analyze sensor data and generate information to support decision-making in construction projects. An envisioned scenario is presented to demonstrate the potential of the system in advancing students’ data analytics and computational thinking skills. The study contributes to existing knowledge in the application of computational thinking and data analytics paradigms in construction engineering education.