The production of precast components in the construction industry is a labor-intensive process. The objectives of this study are to prove the feasibility of using rough set theory to classify and weigh impact attributes, and to develop a model to assess the total quantities of labor needed for precast structural elements using a rough set enhanced K-Nearest Neighbor (KNN). Three main building components (beams, girders, and columns) were collected from the production of precast elements in Taiwan. After trimming and analyzing the basic data, the rough set approach is used to classify and weight the attributes into three levels of impact based on their frequency. A rough set enhanced KNN is accordingly developed, yielding an accuracy rate of 92.36%, which is 8.09% higher than the result obtained when using the KNN algorithm. A practical and effective prediction model would assist managers to estimate the manpower requirement of precast projects.

Civil engineering students often struggle with low motivation and limited practical experience. This study evaluates a blended model integrating team-based learning (TBL) and an immediate response system (IRS) in a senior Civil Construction Methods course. Using one-sample t-tests against the scale neutral midpoint, results show that the TBL–IRS approach was associated with notable levels of behavioral, emotional, and cognitive engagement ( p < 0.001 ), with Cohen’s d ranging from 1.04–2.37 for engagement items and from 1.08 to 1.57 for satisfaction items all substantially exceeding the large-effect threshold ( d > 0.8 ). Learning satisfaction and perceived effectiveness were likewise substantially above. Regression analyses further confirmed a sequential predictive chain engagement was associated with satisfaction; engagement was associated with learning effectiveness, and satisfaction in turn was associated with effectiveness with all three models demonstrating large explanatory power. These findings provide evidence that the TBL–IRS blended model as an effective approach to enhancing motivation and learning outcomes in practice-oriented civil engineering courses, and propose an integrated framework linking engagement, satisfaction, and effectiveness to advance active learning theory in engineering education.