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To understand factors that influence successful practitioner participation in meeting the course support needs of instructors, we utilized a survey to conduct an empirical analysis to model the critical success path of practitioners’ support for student development in practitioner-instructor collaborations. Our results indicated that student-related factors are significant and have a moderate influence. Also, instructor-related factors have a significant impact and large effect on student-related factors. Findings can inform the design and management of practitioners’ provision of instructors’ course support needs. These insights aid student development through practitioner-instructor collaborations. 

Classification of construction resource states, using sensor data analytics, has implications for improving informed decision-making for safety and productivity. However, training on sensor data analytics in construction education faces challenges owing to the complexity of analytical processes and the large stream of raw data involved. This research presents the development and user evaluation of ActionSens, a block-based end-user programming platform, for training students from construction-related disciplines to classify resources using sensor data analytics. ActionSens was designed for construction students to perform sensor data analytics such as activity recognition in construction. ActionSens was compared to traditional tools (i.e., combining Excel and MATLAB) used for performing sensor data analytics in terms of usability, workload, visual attention, and processing time using the System Usability Scale, NASA Task Load Index, eye-tracking, and qualitative feedback. Twenty students participated, performing data analytics tasks with both approaches. ActionSens exhibited a better user experience compared to conventional platforms, through higher usability scores and lower cognitive workload. This was evident through participants' interaction behavior, showcasing optimized attentional resource allocation across key tasks. The study contributes to knowledge by illustrating how the integration of construction domain information into block-based programming environments can equip students with the necessary skills for sensor data analytics. The development of ActionSens contributes to the Learning-for-Use framework by employing graphical and interactive programming objects to foster procedural knowledge for addressing challenges in sensor data analytics. The formative evaluation provides insights into how students engage with the programming environment and assesses the impact of the environment on their cognitive load. 

To understand demographic variations and cater for varying user preferences, we evaluated instructors’ demographic variations on a web-based platform for connecting instructors with practitioners for student development. Both objective and subjective measures were adopted to investigate age- and gender-related differences in gaze behavior, task completion time, perceived cognitive load, perceived usability, and trust. Compared to male instructors, female instructors had higher fixation counts, longer task completion times, and statistically significant longer fixation duration. Female instructors gave higher usability and trust ratings but reported a higher cognitive workload. Compared to Generation Y instructors, Generation X instructors had longer fixation duration, higher fixation count, and statistically longer task completion time. Generation X instructors reported high cognitive load, lower usability, and trust ratings. The study also reveals demographic differences in parameters that instructors focused on while connecting with practitioners via a web platform. It is important that web designers consider gender and age differences and preferences, as well as other demographic variations, when designing web platforms. 

The construction industry is increasingly harnessing sensing technologies to overcome manual data collection limitations and address the need for advanced data analysis. This places an aggravated demand for associated skills to interpret sensor data. Yet, a substantial gap exists between the level of academic preparation and the actual needs of the industry, leading to an underprepared workforce. In this study, ActionSens, a Block-Based Programming Environment, is implemented as an educational tool that combines sensor data from Inertial Measurement Units with machine learning algorithms. This integration enables the classification of construction activities, offering construction students a platform to explore and learn about sensor data analytics. However, in a pedagogical setting, an enhanced learning experience can be achieved through the integration of automated classification models that intelligently detect learners’ focus with the potential to provide context-specific support. This study utilizes 19 construction students’ eye-tracking data to train and evaluate machine learning models to detect learners’ visual focus on specific Areas of Interest within ActionSens. Ensemble, Neural Network, and K-Nearest Neighbor performed the best for both raw and SMOTE-oversampled datasets. The Ensemble had an edge in recognizing Areas of Interest, achieving top precision, recall, F1-score, and AUC in the oversampled data. 

We designed and developed our web platform, ConPEC, to bridge the gap between instructors and practitioners in the construction industry. Subsequently, we recruited 20 construction instructors to interact with the ConPEC platform for evaluation purposes. Results showed the potential for ConPEC to enhance academic pedagogy by providing instructors with improved access to practitioners and fostering a blend of theory and practical knowledge needed in industry. Users perceived ConPEC as useful, user-friendly, and likely to be adopted.