An official website of the United States government
Data analytics and computational thinking are essential for processing and analyzing data from sensors, and presenting the results in formats suitable for decision-making. However, most undergraduate construction engineering and management students struggle with understanding the required computational concepts and workflows because they lack the theoretical foundations. This has resulted in a shortage of skilled workforce equipped with the required competencies for developing sustainable solutions with sensor data. End-user programming environments present students with a means to execute complex analysis by employing visual programming mechanics. With end-user programming, students can easily formulate problems, logically organize, analyze sensor data, represent data through abstractions, and adapt the results to a wide variety of problems. This paper presents a conceptual system based on end-user programming and grounded in the Learning-for-Use theory which can equip construction engineering and management students with the competencies needed to implement sensor data analytics in the construction industry. The system allows students to specify algorithms by directly interacting with data and objects to analyze sensor data and generate information to support decision-making in construction projects. An envisioned scenario is presented to demonstrate the potential of the system in advancing students’ data analytics and computational thinking skills. The study contributes to existing knowledge in the application of computational thinking and data analytics paradigms in construction engineering education.
more »
« less
This content will become publicly available on January 1, 2026
Enhancing computational thinking in construction education: The role of sensor data analytics with block-based programming
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.
more »
« less
- PAR ID:
- 10625033
- Publisher / Repository:
- Journal of Information Technology in Construction
- Date Published:
- Journal Name:
- Journal of Information Technology in Construction
- Volume:
- 30
- ISSN:
- 1874-4753
- Page Range / eLocation ID:
- 65 to 91
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
With technologies changing faster than ever before, engineering faculty must continuously update the technologies they use and teach to students to meet accreditation requirements and keep up with industry standards. Many do not, however. Additionally, existing models of technology adoption do not account for all variability within intention to use a technology, nor its actual use. Informed by the Unified Theory of Acceptance and Use of Technology (UTAUT), this study examined which constructs from prior models apply to engineering faculty’s adoption of industry-specific technologies, as well as other factors influencing faculty adoption of these technologies for their teaching or research. We interviewed 21 engineering faculty at a Midwestern United States STEM-focused institution about their adoption of engineering technologies. Deductive and inductive coding were used to identify themes within the qualitative data. Constructs from existing models were confirmed to influence faculty engineering technology adoption. We also identified specific Facilitating Conditions (Other People, Digital Resources, Non-Digital Resources, Time, and Formal Training) that faculty leverage to adopt new engineering technologies, and uncovered two additional themes—Access and Personal Traits, including several component traits (Persistence, Humility, Self Efficacy, Growth Mindset, Ambiguity Acceptance, and Curiosity) that influence faculty engineering technology adoption. We propose a new Theory of Faculty Adoption of Engineering Technologies specific to faculty adoption of new engineering technologies. These findings have the potential to help universities determine how to effectively support faculty in providing their students with relevant technological skills for entry into the engineering workforce.more » « less
-
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.more » « less
-
Abstract Computational thinking is acknowledged as an essential competency for everyone to learn. However, teachers find it challenging to implement the existing learning approaches in K-12 settings because the existing approaches often focus on teaching computing concepts and skills (i.e., programming skills) rather than on helping students develop their computational thinking competency—a competency that can be used across disciplinary boundaries in accordance with curriculum requirements. To address this need, the current study investigated how game-based learning influenced middle school students’ learning processes, particularly on the development of computational thinking competency, self-efficacy toward computational thinking, and engagement during gameplay. Additionally, the study examined how these outcomes were moderated by individual differences. We observed evidence that the gaming experience influenced students’ computational thinking self-efficacy, but not computational thinking competency or game-based engagement. Compared to age (grade) and prior gaming experience, gender tended to play a more important role in moderating students’ computational thinking competency, self-efficacy toward computational thinking competency, and game-based engagement. Implications and possible directions for future research regarding using game-based learning to enhance computational thinking competency are discussed.more » « less
