Award Abstract # 2048394
Collaborative Research: Creating and testing data science learning tools for secondary students with disabilities

NSF Org: DRL
Division of Research on Learning in Formal and Informal Settings (DRL)
Recipient: UNIVERSITY OF MAINE SYSTEM
Initial Amendment Date: April 1, 2021
Latest Amendment Date: May 20, 2021
Award Number: 2048394
Award Instrument: Standard Grant
Program Manager: Leilah Lyons
llyons@nsf.gov
 (703)292-8637
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: $303,549.00
Total Awarded Amount to Date: $303,549.00
Funds Obligated to Date: FY 2021 = $303,549.00
History of Investigator:
  • Nicholas Giudice (Principal Investigator)
    nicholas.giudice@maine.edu
Recipient Sponsored Research Office: University of Maine
5717 CORBETT HALL
ORONO
ME  US  04469-5717
(207)581-1484
Sponsor Congressional District: 02
Primary Place of Performance: University of Maine
Orono
ME  US  04469-5717
Primary Place of Performance
Congressional District:
02
Unique Entity Identifier (UEI): PB3AJE5ZEJ59
Parent UEI:
NSF Program(s): ITEST-Inov Tech Exp Stu & Teac
Primary Program Source: 1300XXXXDB H-1B FUND, EDU, NSF
Program Reference Code(s): 097Z, 8212
Program Element Code(s): 722700
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.076

ABSTRACT

The main goal of this collaborative project is to create and evaluate a universally accessible data science infrastructure for high-school-aged learners, with a focus on students with disabilities. Data science is critical in the development of industry-relevant computational thinking skills. Computing initiatives, including data science, are rapidly growing at the preschool-12th grade level because of the compelling career pathways that data science skills provide. A careful investigation into already-at-scale data science initiatives shows that such tools and curriculum are largely not accessible to individuals with disabilities, nor do they have a strong foundation of human factors evidence supporting their designs. These issues are crucial and must be resolved for workforce equity and a diverse science, technology, engineering, and mathematics (STEM) pipeline.

This project will bring together investigators in computer science, mechanical engineering, education, social science, and cognitive neuroscience to rethink the tools that support the teaching and learning of data science at the high school level. The overarching goal will be to create and evaluate data science tools and curriculum that are not just in legal compliance for accessibility, but that carefully take into account the needs of learners, including those with disabilities. This project will involve numerous strategic partnerships including two schools for students with disabilities, the Disabilities, Opportunities, Internetworking, and Technology Center, and an engaged advisory board representing computing accessibility (AccessComputing), data science industry (RStudio), and data science governance (Association for Computing Machinery Data Science Task Force). The rigorous research plan will include iterative, user-focused development, empirical quantitative investigations, qualitative focus groups, and a culminating in-classroom field study, targeting an estimated total of 385 students, 105 teachers, and 30 industry professionals as participants across all studies. By creating, deploying, and rigorously evaluating the first data science tool and curriculum that is accessible to all, the project intends to help create equitable pathways for all students to enter the field of data science. 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 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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Brown, Justin R. and Doore, Stacy A. and Dimmel, Justin K. and Giudice, Norbert and Giudice, Nicholas A. "Comparing Natural Language and Vibro-Audio Modalities for Inclusive STEM Learning with Blind and Low Vision Users" In The 25th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 23), , 2023 https://doi.org/10.1145/3597638.3608429 Citation Details
Doore, Stacy A and Brown, Justin R and Imai, Saki and Dimmel, Justin K and Giudice, Nicholas A "Non-Visual Interfaces for Visual Learners: Multisensory Learning of Graphic Primitives" IEEE Access , v.12 , 2024 https://doi.org/10.1109/ACCESS.2024.3513712 Citation Details
Stefik, Andreas and Allee, Willliam and Contreras, Gabriel and Kluthe, Timothy and Hoffman, Alex and Blaser, Brianna and Ladner, Richard "Accessible to Whom? Bringing Accessibility to Blocks" SIGCSE 2024 , 2024 https://doi.org/10.1145/3626252.3630770 Citation Details
Tennison, Jennifer_L and Goswami, Spondita and Hairston, Jesse_R and Merlin_Drews, P. and Smith, Derrick_W and Giudice, Nicholas_A and Stefik, Andreas and Gorlewicz, Jenna_L "Bridging the Gap of Graphical Information Accessibility in Education With Multimodal Touchscreens Among Students With Blindness and Low Vision" Journal of Visual Impairment & Blindness , v.117 , 2024 https://doi.org/10.1177/0145482X231217496 Citation Details

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