Need: The construction industry, one of the industries with the largest labor force in the United States (8% of the total workforce), has long suffered from productivity loss, premature exits of workers from the workforce, and safety issues. To address these challenges, the construction industry has begun investing in sensing technologies (e.g., laser scanners, camera drones, global positioning systems, and radio frequency identification) that can enhance access to critical information needed for quick and informed decision making. The need to adopt these sensing technologies is also reinforced by the benefits of improved performance currently realized by other industry sectors (e.g., manufacturing industries). Data analytics and computational thinking are needed to process data from sensors, analyze the data, and present the resulting models in formats suitable for decision making. Unfortunately, most undergraduate construction engineering and management (CEM) students struggle to understand the computational concepts and workflows required, because they lack the understanding of how to translate data into knowledge for supporting decisions. While a small number of institutions have begun adding sensing technologies and data analytics into their undergraduate CEM programs, some of these courses suffer from low enrollment rates, due to high entry barriers to learning computational concepts that are separate from the target context. This in turn has led to a shortage of the future CEM workforce equipped with the required competencies for developing and implementing sustainable solutions with sensor data. Guiding Question: We hypothesize that an end-user programming (EUP) environment (called SensDat), into which we integrate interactive objects (e.g., abstracted real-world resources, such as vehicles, inventory, and workers), can provide ways to facilitate computational thinking within the target context, enabling students to analyze sensor data in approachable ways and understand essential computational concepts without the burden of knowing detailed programming syntax. To transform CEM education using EUP environments, we will address the following key research question: How do CEM students relate to interactive programming objects (within SensDat) to develop computational thinking skills needed to implement sensor data analytics in the construction industry? This focal scientific question, grounding this research, will allow us to situate the utility of EUP environments and to effectively use SensDat in undergraduate engineering education.Outcomes: With SensDat, we anticipate that undergraduate students will be able to: (1) understand the structures, functions, behaviors, states, and relationships between objects on a construction site and how they are related to the safety and productivity of construction projects by exploring them interactively; (2) quickly prototype analytical methods by specifying algorithms with interactive visual objects, and; (3) review analysis results and iteratively develop desirable models for further intelligent algorithms. Broader Impacts: Properly preparing the technically competent future workforce will advance innovation and creativity in industries that are currently adopting sensing technologies. Through this prepared workforce, this study also has implications for promoting technological and digital awareness, and opportunities in industries that have yet to adopt sensing technologies and data analytics. An understanding of EUP environments has broad societal impacts on developing online learning environments to increase engagement and retention.
Abiola Akanmu, Virginia Tech, Blacksburg, VA; Sang Won Lee, Virginia Tech, Blacksburg, VA; Homero Murzi, Virginia Tech, Blacksburg, VA; Mohammad Khalid, Virginia Tech, Blacksburg, VA; Daniel Manesh, Virginia Tech, Blacksburg, VA; Chinedu Okonkwo, UT San Antonio, San Antonio, TX.