Aligning Construction Engineering Education to Industry Needs: A Computational Thinking Approach with Sensing Technologies and Data Analytics

The growing reliance on data-driven decisions in construction has accelerated the use of sensing technologies to improve productivity, safety, and health. Although sensing technologies can offer useful intelligence, their effective use and analysis of the resulting data remain challenging for the workforce because the soaring trajectory of technical complexity demands skills that many lack. The widening skills gap challenges the construction sector, with industry prioritizing practical competencies in sensing technology implementation (STI) and sensor data analytics (SDA), whereas current curricula offer students limited exposure to these skills. This divergence calls into question how construction academic programs can adequately prepare graduates for evolving industry roles. This study employed a cross-sectional survey to compare the perceptions of required skills and preparedness among two groups such as industry professionals and undergraduate construction students. Professionals reported industry needs and academic preparation, and students engaged in a structured intervention using computational thinking–based visual programming tasks reported the relevance of STI and SDA skills and perceived gains postintervention. Additionally, demographic subgroup analyses explored how factors such as gender and race influenced their perceptions. Results revealed that students reported higher perceived development of STI and SDA skills, but professionals rated current graduates as comparatively underprepared for data-intensive construction environments. The study reveals a skills alignment gap between academic training and industry expectations. It emphasizes the role of computational thinking–based interventions in bridging this divide and reinforces the importance of incorporating hands-on, real-world learning into construction curricula to align education with evolving industry needs.