Collaborative Research: Connecting Professional and Educational Communities to Prepare Construction Engineering Students for the Workplace

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While universities equip students with theoretical knowledge for STEM, there can be challenges in employing those theories to solve real-life problems. This challenge has resulted in an imbalance between the preparation of graduates entering the workforce and the demands of the industry. This disconnection is persistent in the construction industry as construction practitioners continue to highlight skill shortages that have resulted in low performance and low productivity. This research project is designed to connect learners with communities of practice thereby giving them access to expert ways of knowing, thinking, reasoning, and solving real-life problems. The research team will develop a tool that connects construction engineering programs with communities of practice thereby enabling instructors access to industry practitioners with the appropriate expertise to meet their practical course-support needs (e.g., site visits, guest lectures, and mentors for capstone projects). As learners interact with communities of practice, this has the potential to inform the ways learners perceive the profession and the development of their own professional identity. These two phenomena will be examined through a qualitative study. This project is designed to create a collaborative network (called ConPEC) to investigate how the accessibility of construction industry practitioners to instructors, influences construction engineering students’ disciplined perception and professional identity development. The ConPEC platform will employ machine learning algorithms and complex data analysis to allow for pairing instructors with their community of practice. To achieve this, the research will first investigate the practical course-support needs of construction engineering instructors and the characteristics of industry practitioners. Next, the ConPEC framework will be designed to include learning-driven algorithms to exploit dynamic matching patterns between instructors and industry practitioners. Finally, the team will use semi-structured interviews with both students and industry practitioners to gather qualitative data that will be analyzed using Grounded Theory. The research team will examine changes in students’ disciplined perception and professional identity development.This project is supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent challenges in education.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.