Award Abstract # 2055609
Collaborative Research: Computational Modeling for Integrating Science and Engineering Design: Model Construction, Manipulation, and Exploration

NSF Org: DRL
Division of Research on Learning in Formal and Informal Settings (DRL)
Recipient: SRI INTERNATIONAL
Initial Amendment Date: May 6, 2021
Latest Amendment Date: July 7, 2023
Award Number: 2055609
Award Instrument: Continuing Grant
Program Manager: Amy Wilson
amywilso@nsf.gov
 (703)292-2606
DRL
 Division of Research on Learning in Formal and Informal Settings (DRL)
EDU
 Directorate for STEM Education
Start Date: June 1, 2021
End Date: May 31, 2026 (Estimated)
Total Intended Award Amount: $989,017.00
Total Awarded Amount to Date: $989,017.00
Funds Obligated to Date: FY 2021 = $648,071.00
FY 2023 = $340,946.00
History of Investigator:
  • Satabdi Basu (Principal Investigator)
    satabdi.basu@sri.com
  • Kevin McElhaney (Co-Principal Investigator)
Recipient Sponsored Research Office: SRI International
333 RAVENSWOOD AVE
MENLO PARK
CA  US  94025-3493
(609)734-2285
Sponsor Congressional District: 16
Primary Place of Performance: SRI INTERNATIONAL
333 Ravenswood Ave
Menlo Park
CA  US  94025-3493
Primary Place of Performance
Congressional District:
16
Unique Entity Identifier (UEI): SRG2J1WS9X63
Parent UEI: SRG2J1WS9X63
NSF Program(s): CSforAll-Computer Sci for All,
ECR-EDU Core Research
Primary Program Source: 04002122DB NSF Education & Human Resource
04002324DB NSF STEM Education
Program Reference Code(s): 023Z, 8817
Program Element Code(s): 134Y00, 798000
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.076

ABSTRACT

Computational Modeling for Integrating Science and Engineering Design (CMISE) will conduct a series of experiments to systematically compare different computational modeling activities on 5th and 6th grade students? engineering design processes, their understanding of engineering, science and computational thinking concepts, as well as science teachers? confidence and ability to implement integrated STEM and computing curricula. Computational modeling involves a high cognitive load, and research to date is unclear whether the payoff primarily entails learning computing or whether students? science and engineering learning benefit as well. CMISE will investigate how different types of computational modeling activities promote integrated student learning of science and engineering. CMISE will have immediate impacts on STEM + Computing offerings for the Metro Nashville Public School district where the project will be conducted; broadly it will also help strengthen and grow a diverse STEM workforce by bringing authentic and compelling science and engineering opportunities to fifth and sixth grade students. This project will also provide designers and researchers with empirical evidence for how to effectively integrate computer modeling with science and engineering learning activities in different settings. The CMISE curriculum and teacher support materials will be made freely available through project website, allowing these resources to reach a wide range of teachers beyond those included in the study.

CMISE will leverage a previously developed and refined Next Generation Science Standards-aligned curriculum unit that integrates the Earth Science concept of urban water runoff with a meaningful engineering design problem for fifth- and sixth grade students to conduct fundamental research to better understand how different types of computational modeling activities mediate the connections between science and engineering learning. CMISE will conduct a series of design experiments to systematically compare the affordances of three computational modeling activities on students? engineering design processes, their understanding of engineering, science and computational thinking (CT) concepts and practices, as well as science teachers? confidence and ability to implement integrated STEM and computing curricula. The three activities being compared are computational model (CM) construction (where students model a science phenomenon in a given programming language), CM manipulation (where students inspect either the code or the simulation for a given CM of a science phenomenon), and CM exploration (where students explore a given simulation of a science phenomenon without viewing the underlying code). CMISE adopts a strong theoretical framing and a systematic design experiment approach to contribute to the learning theory of how students interact with and learn using different types of computational modeling activities. It will apply quantitative and qualitative analysis methods to combine established statistical analysis methods with novel analytics approaches and derive relations between students' learning behaviors and performance in the three experimental conditions. This design experiment studies will disentangle how these conditions influence the synergistic learning of science, engineering, and CT.

This project is co-funded by the EHR Core Research (ECR) and CS for All: Research and RPPs programs. ECR supports work that advances fundamental research on STEM learning and learning environments, broadening participation in STEM, and STEM workforce development.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Basu, Satabdi and Rachmatullah, Arif and McElhaney, Kevin and Alozie, Nonye and Yang, Hui and Hutchins, Nicole and Biswas, Gautam and Mills, Kelly "A Comparison of Computational Practices and Student Challenges Across Three Types of Computational Modeling Activities Integrating Science and Engineering" , 2024 https://doi.org/10.22318/icls2024.549121 Citation Details
McElhaney, Kevin W and Basu, Satabdi and Alozie, Nonye and Hutchins, Nicole and Rachmatullah, Arif and Mills, Kelly and Biswas, Gautam "Broadening Participation in STEM-based Computational Modeling by Leveraging Alternatives to Programming" , 2024 https://doi.org/10.22318/icls2024.467572 Citation Details

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