| NSF Org: |
DRL Division of Research on Learning in Formal and Informal Settings (DRL) |
| Recipient: |
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| Initial Amendment Date: | January 11, 2023 |
| Latest Amendment Date: | January 11, 2023 |
| Award Number: | 2241786 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Lin Lipsmeyer
llipsmey@nsf.gov (703)292-7076 DRL Division of Research on Learning in Formal and Informal Settings (DRL) EDU Directorate for STEM Education |
| Start Date: | March 1, 2023 |
| End Date: | February 28, 2027 (Estimated) |
| Total Intended Award Amount: | $334,245.00 |
| Total Awarded Amount to Date: | $334,245.00 |
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| Recipient Sponsored Research Office: |
300 TURNER ST NW BLACKSBURG VA US 24060-3359 (540)231-5281 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
300 TURNER ST NW BLACKSBURG VA US 24060-3359 |
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Performance Congressional District: |
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| NSF Program(s): | ITEST-Inov Tech Exp Stu & Teac |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.076 |
ABSTRACT
As the construction industry gears toward adopting data sensing technologies, there is a demand for creating and sustaining a workforce with skills for implementing the technologies and analyzing the resulting data to support decision-making. It is also essential to improve awareness of this Science, Technology, Engineering, and Mathematics (STEM) career option among all K-12 students, develop their understanding of the applications of data sensing technologies and improve their computational thinking skills in manipulating and using data. This project aims to investigate an immersive virtual reality-based learning environment for developing middle school students? computational thinking skills necessary to address construction challenges and improving students? engagement and attitudes towards STEM-related careers in the construction industry. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.
This research will develop a DAta Sensing Learning EnvironmenT (called DASLET) to investigate how students? computational thinking can be developed in addressing construction industry challenges with data sensing technologies and improve their engagement and attitudes toward STEM-related careers. Within DASLET, students can learn how to safely work with different data sensing technologies on a virtual construction site, translate sensor data into computational rules and extract meaningful information to support decision-making. The research will first develop a virtual reality-based learning environment that can facilitate tangible interaction with data sensing technologies to equip middle school students with skills for addressing construction risks. Next, working closely with middle school teachers, strategies will be developed for adapting applications of data sensing technologies in construction and the proposed learning environment to the middle school curriculum. Using mixed methods and multilevel modeling, the research team will implement DASLET with approximately 40 teachers and 120 students, and develop theories to explain how embodied interaction within the learning environment can enhance students? computational thinking, improve engagement with data sensing technologies, and interest in data sensing and STEM-related careers in the built environment. The development of DASLET will involve industry practitioners and teachers from diverse groups including females and underrepresented minorities. It will be implemented in the schools and summer camps that serve students from groups that are underrepresented in STEM.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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