Award Abstract # 2123346
Collaborative Research: HDR DSC: Infusing community-centered data science into undergraduate engineering curricula

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: UNIVERSITY OF NEW MEXICO
Initial Amendment Date: August 5, 2021
Latest Amendment Date: August 5, 2021
Award Number: 2123346
Award Instrument: Standard Grant
Program Manager: Sylvia Spengler
sspengle@nsf.gov
 (703)292-7347
IIS
 Division of Information & Intelligent Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2021
End Date: September 30, 2025 (Estimated)
Total Intended Award Amount: $130,000.00
Total Awarded Amount to Date: $130,000.00
Funds Obligated to Date: FY 2021 = $130,000.00
History of Investigator:
  • Fernando Moreu (Principal Investigator)
    fmoreu@unm.edu
Recipient Sponsored Research Office: University of New Mexico
1 UNIVERSITY OF NEW MEXICO
ALBUQUERQUE
NM  US  87131-0001
(505)277-4186
Sponsor Congressional District: 01
Primary Place of Performance: University of New Mexico
1700 Lomas Blvd. NE, Suite 2200
Albuquerque
NM  US  87131-0001
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): F6XLTRUQJEN4
Parent UEI:
NSF Program(s): HDR-Harnessing the Data Revolu
Primary Program Source: 01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 062Z
Program Element Code(s): 099Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

The goal of this project is to develop a curricular framework for data science education and workforce development that is transferable between diverse institutions, so STEM-related programs can plug and play data science lessons with existing curricula without much overhead. These lessons will be created in conjunction with community stakeholders and industry partners to ensure a focus on real-world problem solving and include student organizations in course development to promote flexible learning pathways. The proposed additions to undergraduate STEM education will provide an evidence-based blueprint for best practices in integrating data science with existing engineering curricula. Implementation across multiple engineering departments will result in a significant impact on society through the training of a diverse, globally competitive STEM workforce with high data literacy.

The objectives of this project are to (1) facilitate data science education and workforce development for engineering and related topics, (2) provide opportunities for students to participate in practical experiences where they can learn new skills in a variety of environments, and (3) expand the data science talent pool by enabling the participation of undergraduate students with diverse backgrounds, experiences, skills, and technical maturity in the Data Science Corps. This work will support the Data Science Corps objective of building capacity for education and workforce development to harness the data revolution at local, state, and national levels. The institutions gathered for this project will develop training programs and curate datasets that will be made available so they can be included in undergraduate instruction nationwide. Furthermore, the training materials will be shared with industry partners, facilitating workforce development. The project team will develop a website to house data science training programs, didactic datasets, and other resources for educators. These resources are intended to reduce barrier to entry for faculty seeking to incorporate data science into their instruction, as recruiting and retaining faculty to create and teach integrated introductory courses in data science has been recognized as a significant hurdle by the National Academies.

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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Sanei, Mahsa and Atcitty, Solomon and Moreu, Fernando "Low-Cost Efficient Wireless Intelligent Sensor (LEWIS) for Research and Education" Sensors , v.24 , 2024 https://doi.org/10.3390/s24165308 Citation Details
Malek, Kaveh and Ortíz Rodríguez, Edgardo and Lee, Yi-Chen and Murillo, Joshua and Mohammadkhorasani, Ali and Vigil, Lauren and Zhang, Su and Moreu, Fernando "Design and implementation of sustainable solar energy harvesting for low-cost remote sensors equipped with real-time monitoring systems" Journal of Infrastructure Intelligence and Resilience , v.2 , 2023 https://doi.org/10.1016/j.iintel.2023.100051 Citation Details
Malek, Kaveh and Mohammadkhorasani, Ali and Moreu, Fernando "Methodology to integrate augmented reality and pattern recognition for crack detection" Computer-Aided Civil and Infrastructure Engineering , v.38 , 2023 https://doi.org/10.1111/mice.12932 Citation Details

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