Khalid, Mohammad (2025) Enhancing computational thinking skills of the future construction workforce to perform sensor data analytics with end-user programming environment. PhD thesis, Virginia Polytechnic Institute and State University, USA.
Abstract
Despite being one of the most significant employment hubs of the United States, the construction industry continues to struggle with declining productivity, workplace hazards, and a growing shortage of skilled workers. The expanding range of sensor-based applications triggers a high demand to equip the future workforce with specialised skills. However, for many construction engineering and management graduates, the expanding complexity of data and technology creates comprehension difficulties regarding essential computational concepts and procedural workflows, restricting the workforce's ability to convert data into actionable insights for informed decision-making. To bridge these skill gaps, end-user programming offers considerable potential for learners to execute analytical operations on sensor data through visual programming mechanics, fostering computational thinking skills essential for transforming unstructured sensor data into actionable intelligence. This research explores how block-based programming environments can be integrated into construction engineering and management education to improve technical skills, particularly in sensor data analytics. Using a mixed-method approach, the research first surveyed industry professionals to identify the required competencies. Based on this input, a block-based programming environment was designed to help students learn data analytics using authentic sensor data. The environment's efficiency and effectiveness were evaluated with construction students, focusing on key factors such as usability, cognitive load, and visual attention demand, incorporating eye-tracking and electroencephalography (EEG) methods. The environment was implemented in classrooms for a summative assessment to measure learners' self-efficacy in targeted skills, performance, and technology acceptance, and to examine demographic factors impacting user interaction. The affordances and effectiveness of block-based environments contribute to the Learning-for-Use framework by utilising graphical, interactive programming elements to develop procedural knowledge for solving sensor data analytics problems.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Akanmu, Abiola Abosede |
| Uncontrolled Keywords: | sensor data analytics; sensing technologies; computational thinking; end-user programming; block-based programming; usability; eye-tracking; electroencephalography; ergonomic risks |
| Index terms: | interaction, self-efficacy, construction industry, required competency, demographic factor, construction engineering, effectiveness, employment, productivity, skilled worker, efficiency, affordance, workflow, complexity, technology acceptance, decision-making, classroom, programming, usability, sensor data, United States, technical skill |
| Subjects: | research products and data, innovation and technology management, construction type, programming, human factors and perception, demography, behavioral psychology, Geography, user-centered design, decision analysis, industry analysis, systems engineering, management, performance management, engineering methods, professional development |
| Topics: | Geographical Context, Engineering Principles, Risk Management, Quality Management, Business Strategy, Research Practice, Information Management, Construction Technology, Design Practice, Digital Applications, Urban Studies, Human Resources |
| Descriptive scope: | 3 PCT |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here