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Active back-support exoskeleton has gained recognition as a potential solution to mitigate work- related musculoskeletal disorders. However, their utilization in the construction industry can introduce unintended consequences, such as increased fall hazards. This study examines the implications of using active back-support exoskeleton on fall risk during construction framing tasks, incorporating wearable pressure insoles for data collection. Two experimental conditions were established, one involving the simulation of construction framing tasks with exoskeleton and the other without exoskeleton. These tasks encompassed six subtasks: measuring, assembly, nailing, lifting, moving, and installation. Foot plantar pressure distribution was recorded across various spatial foot regions, including the arch, toe, metatarsal, and heel. Statistical analysis, employing a paired t-test on peak plantar pressure data, revealed that the use of active back-support exoskeleton significantly increased fall risks in at least one of the foot regions for all subtasks, except for the assembly subtask. These findings provide valuable insights for construction stakeholders when making decisions regarding the adoption of active back-support exoskeleton in the industry. Moreover, they inform exoskeleton manufacturers of the need to develop adaptive and customized exoskeleton solutions tailored to the unique demands of construction sites. 

With rising interest in innovative construction methodologies, global construction companies are actively exploring emerging sensing technologies and employing data analytics techniques to draw insights and improve their operations. While numerous educational disciplines employ Block-based Programming Interfaces to enhance domain-specific data-related inquiry and visualization skills, the construction sector has yet to fully explore this practical approach. Introducing block interfaces in construction education may overwhelm newcomers with excessive cognitive load. Past research has primarily relied on subjective measures, overlooking objective indicators for assessing cognitive responses to block interfaces’ interaction elements. This study evaluates the cognitive load induced using InerSens, a Block Programming Interface designed to address authentic construction challenges in ergonomic risk assessment. Electroencephalography is utilized to measure cognitive load, and the results are compared to those of a traditional tool, Excel. Theta Power Spectral Density in the frontal brain region, an indicator of cognitive load, demonstrates that in four out of six tasks, InerSens incurs lower cognitive load than Excel. The findings of this study underscore the potential of InerSens as a viable tool in managing cognitive load efficiency, paving the way for more effective and streamlined sensor data analytics learning experiences for future construction professionals. 

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. 

The construction industry is rapidly changing due to the greater adoption of innovations and technology. This has necessitated changes in the competencies that the industry demands from new graduates. For academia to meet the changing needs of the industry, the inputs of practitioners are needed to complement academic pedagogical efforts. This study leverages the potential of Web 2.0 to develop a web platform called ConPEC to facilitate instructor-practitioner collaborations for enhancing student learning. ConPEC is aimed at providing instructors with equitable access to practitioners, increasing the participation of practitioners in instructors' pedagogical efforts, and enabling greater interaction of students with their communities of practice (CoP). These could facilitate achieving a proper blend of theory and practice in construction engineering education as well as ensure that students possess the competencies that the industry demands. This study demonstrates the efficacy of design principles in designing information systems. This study also demonstrates the usage of the Technology Acceptance Model (TAM) to explain and understand practitioners' acceptance of ConPEC. The findings reveal that practitioners perceived ConPEC to be useful, easy to use, and user-friendly. Practitioners’ behavioral intention-to-use ConPEC is significantly influenced by attitude toward usage, perceived ease of use, and trust. Trust also significantly influenced perceived ease of use. However, perceived usefulness has no direct significant influence on practitioners’ behavioral intention-to-use ConPEC. The study uncovers practitioners' acceptance behavior toward ConPEC which could be leveraged for further system development. The study also provides a framework that can be leveraged in diverse domains to develop similar initiatives aimed at addressing skill gaps in fresh graduates.