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With the growing rate of adoption of sensing technologies in the construction industry, there is an increased need for technically skilled workforce to successfully deploy these technologies on construction projects. Inspired by opportunities offered by mixed reality, this paper presents the development and evaluation of a holographic learning environment that can afford learners an experiential opportunity to acquire competencies for implementing sensing systems on construction projects. To develop the content of the learning environment, construction industry practitioners and instructors were surveyed, and construction industry case studies on the applications of sensing technologies were explored. Findings of the surveys revealed sensing technologies domain-specific skill gap in the construction industry. Further, the findings informed the requirements of the learning environment. Based on these requirements, key characteristics of the learning environment are identified and employed in designing the environment. Still, a formative evaluation is important for developing an effective mixed reality learning environment for teaching domain-specific competencies. Thus, it is imperative to understand the quality, appropriateness, and representativeness of the content of the learning environment. This paper also presents a learnability assessment of the developed mixed reality learning environment. The assessment was conducted utilizing a focus group discussion with construction industry practitioners. Feedback was sought from the participants regarding the reflectiveness of the layout of the virtual environment of an actual construction site and the appropriateness of the represented construction applications. This study contributes to the definition of the type of domain-specific skills required of the future workforce for implementing sensing technologies in the construction industry and how such skills can be developed and enhanced within a mixed reality learning environment.

Mixed reality based environment for learning sensing technology applications in construction