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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. 

The construction industry's shift to data-driven project management has led to the increasing adoption of various sensing technologies. The transition triggers a demand for a workforce skilled in both the technical and analytical aspects of these tools. While sensing technologies and data analytics can support construction processes, the inherent complexity of sensor data processing often exceeds the skill sets of the graduating workforce. Further, integrating sensor-based applications into construction curricula lacks evidence to support effectiveness in training future professionals. Computational thinking-supported data practices can allow construction students to perform sensor data analytics, spanning from data generation to visualization. This pilot study utilizes InerSens, a block programming interface, as a pedagogical tool to develop construction students’ computational thinking through sensor-based ergonomic risk assessment. Twenty-six undergraduate students were engaged in instructional units using wearable sensors, data, and InerSens. The effectiveness of the approach was evaluated by examining students' perceived self-efficacy in sensor data analytics skills, task performance and reflections, and technology acceptance. Results show gains in self-efficacy, positive technology acceptance, and satisfactory performance on course assignments. The study contributes to the Learning-for-Use, constructivism, and constructionism frameworks by integrating computational thinking into graphical and interactive programming objects to develop procedural knowledge and by summatively assessing how construction students learn to address challenges with sensor data analytics. 

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. 

As a precursor to designing the ConPEC platform for stronger industry-academia collaborations, we investigated factors which instructors would consider when collaborating with practitioners to complement their pedagogical efforts. We found that instructors' considerations were influenced by students' preferences and bias, students' career and development, student learning outcomes, curriculum structure, as well as ethnic and gender diversity. Findings inform input for the design of web-based collaborative networks. Also, this study contributes to expanding literature on industry-academia collaborations for workforce development. 

This lessons learned paper delves into the realm of effective student-centered teaching practices within middle and upper-level engineering classes, with the primary goal of enhancing students' acquisition of disciplinary knowledge. The research is anchored by a central inquiry: what student-centered teaching approaches do exemplary engineering faculty employ to promote knowledge-building in their courses, and how do these approaches align with their beliefs about teaching? To address the research question, the study employed the participatory action research (PAR) methodology, which prioritizes the invaluable input and expertise of participants. A diverse group of participants renowned for their teaching excellence was selected from five departments. A total of ten participants were chosen, and data was collected using a variety of methods, including classroom observations, analysis of course materials, surveys, and focus group discussions. Our observations across various courses have revealed common practices employed by instructors to foster effective learning environments. These practices encompass dynamic and diverse class introductions that utilize strategies like revisiting prior content, storytelling, and addressing student well-being to establish a strong foundation for the session. Throughout the class, instructors consistently maintained student engagement through techniques such as group activities, structured interactions, active problem-solving, and thought-provoking question-and-answer sessions. Visual aids and technology were integral in enhancing content delivery. Instructors also ensured the content was relatable by linking lessons to research findings, relatable examples, and familiar landmarks, grounding theoretical concepts in real-life relevance. Personalized support was a priority, with instructors offering targeted feedback to smaller groups and individual students, including one-on-one sessions for additional assistance. Some instructors introduced unique practices such as debate activities, involving students in decision-making processes, cross-course connections, and specialized problem-solving techniques. These diverse approaches collectively underscore the multifaceted strategies instructors employ to create engaging and effective learning experiences. Another significant initiative undertaken in our study involved organizing a summer workshop that provided a platform for instructors to convene and engage in collaborative discussions regarding their teaching practices and their top five teaching priorities. During this workshop, we also deliberated on the preliminary findings from our data collection. The instructors collectively emphasized the importance of getting students engaged in the learning process. We identified several overarching categories of priorities that held relevance for all instructors, including the establishment of personal relationships with students, the effective organization of course content and class activities, strategies for motivating students, and the integration of course content with real-world applications. During the lightning talk, we will share a comprehensive overview of the study's research findings as well as the importance of student-centered teaching practices in engineering education. 

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. 

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. 

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.