Pedagogical Strategies and Critical Success Factors for Enhancing Active Learning of Undergraduate Construction and Surveying Students

Active learning is essential for students in the construction and surveying disciplines due to the practical work nature and technical demands of these professions. This research study aims to identify and evaluate effective pedagogical strategies and critical success factors (CSFs) that can enhance active learning among undergraduate construction and surveying students. Customized e-learning materials based on the Technology, Pedagogy, Content, and Knowledge (TPACK) framework were adopted to improve the learning outcomes and effectiveness for semester-long construction-related research course students. Subsequently, an institutional student-based questionnaire survey was conducted and analyzed using mean score, exploratory factor analysis, and fuzzy synthetic evaluation (FSE). Accordingly, the top five active instructional learning strategies are using examples to reinforce understanding, case studies to encourage critical thinking, videos to improve understanding, connecting course contents to current community events, and creating classroom versions of interactive games. Exploratory factor analysis classified the CSFs into five major factor categories. The FSE results indicated that the top three CSF categories enhancing students’ active learning include electronic tools as learning aids, complementary learning and feedback, and model learning system development. This study provides essential learning environments and components needed to foster active learning among undergraduate construction and surveying students in Hong Kong and worldwide

Cooperative learning · Critical success factor · Critical thinking · Exploratory factor analysis · Exploratory research · Knowledge management · Mathematics education · Structural equation modeling · Teaching method · Computer Science · Evaluation of Teaching Practices · Innovative Teaching Methods · Problem and Project Based Learning · Psychology · Artificial Intelligence

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