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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. 

The fast-growing adoption of sensing technologies in the construction industry has necessitated a demand for workforce with technical skills. This study explores the current state of sensing technologies in the industry and sensing technology education in construction engineering and management programs. The study investigates the agreeability of industry and academia’s perceptions of the integration of sensing technologies in construction engineering and management curricula. The study employs online surveys to capture industry and instructor perceptions of the skills required of graduating construction engineering and management students and the extent of sensing technology education respectively. Comparison of the survey responses reveals differences between sensing technologies and applications deployed in the industry and those taught in construction engineering and management programs. While reinforcing the need for technical skills in the industry, results provide highlights to well-structured sensing technology courses based on required competencies to prepare students for a relevant and successful career in the industry. 

The growth in the adoption of sensing technologies in the construction industry has triggered the need for graduating construction engineering students equipped with the necessary skills for deploying the technologies. One obstacle to equipping students with these skills is the limited opportunities for hands-on learning experiences on construction sites. Inspired by opportunities offered by mixed reality, this paper presents the development of a holographic learning environment that can afford learners an experiential opportunity to acquire competencies for implementing sensing systems on construction projects. The interactive holographic learning environment is built upon the notions of competence-based and constructivist learning. The learning contents of the holographic learning environment are driven by characteristics of technical competencies identified from the results of an online survey, and content analysis of industry case studies. This paper presents a competency characteristics model depicting the key sensing technologies, applications and resources needed to facilitate the design of the holographic learning environment. A demonstrative scenario of the application of a virtual laser scanner for measuring volume of stockpiles is utilized to showcase the potential of the learning environment. A taxonomic model of the operational characteristics of the virtual laser scanner represented within the holographic learning environment is also presented. This paper contributes to the body of knowledge by advancing immersive experiential learning discourses previously confined by technology. It opens a new avenue for both researchers and practitioners to further investigate the opportunities offered by mixed reality for future workforce development.