Exploration of demographic variations is required to develop dynamic web platforms that cater to the varying preferences of diverse users. Hence, this study evaluated instructors’ demographic variations on a web-based platform for connecting 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.

Contribution: This research category full paper contributes to the body of knowledge by identifying competencies in terms of knowledge, skills, and abilities that could potentially guide future investigations into how industry and academia perceive the essential competencies for implementing sensing technologies in the construction industry. The identified competencies provide valuable insights into the training required for both current and future workforces to meet the demands of sensing technology in the workplace. Additionally, they highlight the need for further investigation into learning technologies that can support the acquisition of these competencies. The study also expands the application of the Technology-Organization-Environment framework in the context of the competencies for implementing sensing technologies within the construction industry. Background: Data sensing technologies, such as laser scanners, radio frequency identification systems, cameras, and unmanned aerial vehicles, are increasingly being adopted in the construction industry to improve productivity, safety, and quality control of construction projects. The continuous adoption of these technologies necessitates a well-equipped workforce with the requisite competencies to sustain innovations in the industry. However, despite the importance of competent workforces in adopting sensing technologies in the construction industry, there is limited knowledge of the competencies for implementing these technologies. This study aims to fill this gap by identifying competencies in terms of knowledge, skills, and abilities required to implement sensing technologies in the construction industry through the lens of the Technology-Organization-Environment framework, thereby providing practical implications for the industry. Research Questions: (1) what are the applications of sensing technologies in the construction industry? and (2) what are the competencies requisite for implementing sensing technologies? Methodology: This study adopted a qualitative literature review to identify studies on sensing technologies' applications in the construction industry. Content analysis was used to extract competencies in terms of knowledge, skills, and abilities. The extraction was performed by examining the application of sensing technologies through the Technology-Organization-Environment framework, emphasizing the interplay of factors in the technological, organizational, and environmental contexts. Findings: The findings of this study showed the competencies encompassing nineteen knowledge, eight skills, and twelve abilities required to implement sensing technologies in the construction industry.

Contribution: This research category full paper contributes to the body of knowledge by revealing the areas of agreement on competencies for implementing sensing technologies by instructors and industry practitioners in the construction industry. The findings will help the instructors equip the future workforce with the competencies to implement sensing technologies through integrated curriculum development. Additionally, the findings could help industry practitioners update the competencies of the current workforce in the construction industry for effective sensing technologies deployment. Consequently, this study's findings will help reduce the misalignment in competencies acquired by the future workforce and the industry-specific competencies for implementing sensing technologies. Background: Adopting data sensing technologies like laser scanners, radio frequency identification systems, cameras, and unmanned aerial vehicles in the construction industry requires a workforce with the necessary competencies. To address this, curriculum content should be balanced to train emerging workers to meet industry expectations for implementing these technologies. This balance is crucial to minimizing the resources employers spend on post-hire training. This study compares the perspectives of instructors and industry practitioners on the competencies required for implementing sensing technologies in the construction industry. Research Question: What are the perspectives of industry practitioners and instructors on the competencies for implementing sensing technologies in the construction industry? Methods: This study employed a three-round Delphi survey administered to instructors in accredited higher education institutions and construction industry practitioners in the United States. Through the successive Delphi survey rounds, the study compares the instructors' and construction industry practitioners' perspectives on essential competencies required for implementing sensing technologies in the construction industry. Findings: The first survey round shows the suitability of instructors' and industry practitioners' expertise in partaking in the study. The second survey round shows a trend toward convergence, indicating potential similarities between instructors' and industry practitioners' perspectives on specific competencies required for implementing sensing technologies in the construction industry. The third round showed the qualitative feedback from instructors and industry practitioners on their perception of the competencies for implementing sensing technologies in the construction industry.

This research category work-in-progress paper presents industry and academic perceptions of competencies for human-robot collaboration in the construction industry. Perceptions of competencies by industry professionals and academic experts vary significantly. Industry professionals prioritize competencies like technical skills and hands-on experience that directly enhance productivity and profitability. In contrast, academic experts prioritize scientific and theoretical understanding and intellectual development to promote innovation through research and education. Identifying the perceptional differences and consensus is crucial for successfully integrating robotics in the construction industry and developing training programs to prepare the current and future workforce. This study investigates the perceptions of industry professionals and academic experts regarding competencies for human-robot collaboration in construction to identify areas of agreement and divergence. A three-round Delphi survey was conducted to collect industry professionals' and academic experts' perceptions concerning the competencies for human-robot collaboration in construction. Cronbach's alpha was used to assess the reliability and internal consistency of the data collected, while the standard deviation and interquartile range were used to measure the consensus of the expert's opinion on competency for human-robot collaboration in construction. Thirteen industry practitioners and fourteen academic experts participated in the survey. Results of the Delphi survey reveal areas of consensus in the perceptions of industry and academia concerning some competencies for human-robot collaboration, which include human-robot interface ranked as the most significant HRC knowledge, safety management, technical skills, regulation standards and compliance, data analytics and management, and application of machine learning algorithms skills ranked equally in different positions, and safety awareness ranked as the most important ability for HRC. There are differences in the perceptions of both panels of experts concerning the remaining competencies for human-robot collaboration. This study underscores the perspective of industry professionals and academic experts on the competencies crucial for facilitating safe and effective collaboration with robots in the construction industry.

This research-to-practice full paper identifies the essential competencies for human-robot collaboration in the construction industry through a sociotechnical systems theory perspective. The construction industry grapples with significant challenges, including a shortage of skilled workers, low productivity, efficiency, and safety issues that impede its progress and growth. The integration of robots into the construction industry presents a promising solution to address these issues, thereby necessitating collaboration between humans and robots in executing construction tasks. Despite the advantages and roles played by robotic automation, there have been scarce efforts to identify essential competencies required to prepare the current and future workforce for effective collaboration with robots in construction. This study fills this gap by identifying essential competencies in the form of knowledge, skills, and abilities necessary for successful human-robot collaboration in construction. Using the sociotechnical systems theory as a framework, a qualitative literature review was conducted to establish and correlate the constructs of sociotechnical systems theory and elements of human-robot collaboration. Content analysis was employed to identify the elements of sociotechnical systems theory, human-robot collaboration, and robot task applications in construction, leading to the identification of key competencies. The study reveals a set of competencies for effective human-robot collaboration, including twenty knowledge, ten skills, and twelve abilities essential for implementing human-robot collaboration in the construction industry. These findings offer valuable insights for designing training programs and developing guidelines to facilitate successful human-robot collaboration in the construction industry. The competency model unveiled in the study could be incorporated into construction engineering and management curricula, providing a foundation for developing training initiatives targeting the current workforce and preparing the future workforce for collaborative engagements with robots in the construction industry. Recognizing the specific knowledge, skills, and abilities needed for human-robot collaboration in construction is pivotal for enhancing the efficiency and success of robotics implementation in the industry. Integrating these competencies into educational curricula and professional development programs equips the workforce to adapt to technological advancements and positions the industry for sustainable growth and improved project outcomes.

The construction industry is a new avenue for big data and data science with sensors and cyber-physical systems deployed in the field. Construction students need to develop computational thinking skills to help make sense of this data, but existing data science environments designed with textual programming languages create a significant barrier to entry. To bridge this gap, we introduce Octave, an end-user programming environment designed to help non-expert programmers analyze spatiotemporal data (e.g., as gathered by a GPS sensor) in an interactive graphical user interface. To aid exploration and understanding, Octave's design incorporates a high degree of liveness, highlighting the interconnection between data, computation, and visualization. We share the underlying design principles behind Octave and details about the system design and implementation. To evaluate Octave, we conducted a usability study with students studying construction. The results show that non-programmer construction students were able to learn Octave easily and were able to effectively use it to solve domain-specific problems from construction education. The participants appreciated Octave's liveness and felt they could easily connect it to real-life problems in their field. Our work informs the design of future accessible end-user programming environments for data analysis targeting non-experts.