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As a precursor to designing the ConPEC platform for stronger industry-academia collaborations, we investigated factors which instructors would consider when collaborating with practitioners to complement their pedagogical efforts. We found that instructors' considerations were influenced by students' preferences and bias, students' career and development, student learning outcomes, curriculum structure, as well as ethnic and gender diversity. Findings inform input for the design of web-based collaborative networks. Also, this study contributes to expanding literature on industry-academia collaborations for workforce development. 

The need to prepare students for the workplace, shortage of skilled labor, and fast-paced changes in the industry necessitate improvements in the pedagogical frameworks of educational communities. Practitioners are required to provide practical insights, rigor, and realism to complement academia pedagogic efforts in construction education. However, this is being plagued by several complexities. Leveraging advances in computational techniques, this paper presents the considerations of practitioners and instructors in workforce development collaborations as inputs for a graphical user interface of a technology-driven matching platform for connecting professional and educational communities. Practitioners’ considerations are students and specific course-support related, while instructors’ considerations are related to practitioner suitability, project, and company characteristics. The study contributes to human factors principles in user interface design as well as user-centered design principles by highlighting information requirements of a collaborative network of instructors and practitioners. The findings of this study also provide insights to enhance industry-academia collaborations. 

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.