Data Analytics and Computational Thinking Skills in Construction Engineering and Management Education: A Conceptual System
Publication: Construction Research Congress 2022
ABSTRACT
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.
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Published online: Mar 7, 2022
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- Mohammad Khalid, Anthony Yusuf, Abiola Akanmu, Homero Murzi, Ibukun Awolusi, The Impact of Individual Differences in Developing Computational Thinking and Sensor Data Analytics Skills in Construction Engineering Education, International Journal of Construction Education and Research, 10.1080/15578771.2024.2404969, 20, 4, (483-500), (2024).
- Mohammad Khalid, Abiola Akanmu, Homero Murzi, Sang Won Lee, Ibukun Awolusi, Daniel Manesh, Chinedu Okonkwo, Industry Perception of the Knowledge and Skills Required to Implement Sensor Data Analytics in Construction, Journal of Civil Engineering Education, 10.1061/JCEECD.EIENG-1902, 150, 1, (2024).
- Mohammad Khalid, Abiola Akanmu, Adedeji Afolabi, Homero Murzi, Ibukun Awolusi, Philip Agee, InerSens: A Block-Based Programming Platform for Learning Sensor Data Analytics in Construction Engineering Programs, Journal of Architectural Engineering, 10.1061/JAEIED.AEENG-1758, 30, 3, (2024).