Award Abstract # 2045561
CAREER: Engaging Rural Students with Next Generation Physiological Interfaces

NSF Org: DRL
Division of Research on Learning in Formal and Informal Settings (DRL)
Recipient: UNIVERSITY OF ALABAMA
Initial Amendment Date: January 27, 2021
Latest Amendment Date: July 16, 2024
Award Number: 2045561
Award Instrument: Continuing Grant
Program Manager: Margret Hjalmarson
mhjalmar@nsf.gov
 (703)292-5186
DRL
 Division of Research on Learning in Formal and Informal Settings (DRL)
EDU
 Directorate for STEM Education
Start Date: May 1, 2021
End Date: April 30, 2026 (Estimated)
Total Intended Award Amount: $798,050.00
Total Awarded Amount to Date: $650,600.00
Funds Obligated to Date: FY 2021 = $231,598.00
FY 2022 = $122,774.00

FY 2023 = $125,968.00

FY 2024 = $170,260.00
History of Investigator:
  • Chris Crawford (Principal Investigator)
    crawford@cs.ua.edu
Recipient Sponsored Research Office: University of Alabama Tuscaloosa
801 UNIVERSITY BLVD
TUSCALOOSA
AL  US  35401
(205)348-5152
Sponsor Congressional District: 07
Primary Place of Performance: University of Alabama Tuscaloosa
801 University Blvd
Tuscaloosa
AL  US  35486-0005
Primary Place of Performance
Congressional District:
07
Unique Entity Identifier (UEI): RCNJEHZ83EV6
Parent UEI: RCNJEHZ83EV6
NSF Program(s): CSforAll-Computer Sci for All,
ITEST-Inov Tech Exp Stu & Teac
Primary Program Source: 04002122DB NSF Education & Human Resource
04002223DB NSF Education & Human Resource

04002324DB NSF STEM Education

04002425DB NSF STEM Education

04002526DB NSF STEM Education

1300CYXXDB H-1B FUND, EDU, NSF

1300XXXXDB H-1B FUND, EDU, NSF

1300XXXXDB H-1B FUND, EDU, NSF
Program Reference Code(s): 1045, 9150
Program Element Code(s): 134Y00, 722700
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.076

ABSTRACT

Next generation physiological computing systems use sensors to measure data like brain activity or heart activity. These emerging systems are at the forefront of human-computer interaction applications involving health technologies, adaptive smart environments, self-regulation, security and authentication. Future STEM professionals are needed to design applications and tools that rely on physiological computing. In addition, physiological sensors are motivating opportunities for students to learn about computer science, data processing, technology, and design. This project will provide rural students and teachers with hands-on learning experiences in physiological computing. The project will investigate how students learn about physiological data processing and computational concepts. Products will include learning modules for high school science and technology courses. Findings from the proposed research will inform future K-12 work at the intersection of human physiology and technology. This project is funded by the CS for All: Research and RPPs program. This project is also 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.

Students are expected to gain physiological data processing and computational skills while engaging with the technology developed in the project. More specifically, physiological sensors engage students in learning about wave properties and other signal processing topics. Physiological computing also requires interdisciplinary learning in data processing, computational thinking, and scientific phenomenon. This project will explore physiological computing in high school classrooms through multiple studies that explore various ways to engage students with physiological computing such as (1) pre-recorded and real-time data (2) integrating physiological data processing and computational concepts, (3) textual and visual programming interfaces, and (4) digital and physical feedback components. The project includes the following research questions. First, how does hands-on experience with pre-recorded and real-time physiological data influence students? ability to gain knowledge of physiological data processing concepts? How do these experiences influence students? interest in STEM? Second, to what extent does integrating computational and data processing concepts build computational thinking and physiological computing skills? When compared to a visual interface, how will a textual interface for physiological data processing influence computational thinking, knowledge of physiological data processing concepts, and interest in STEM? Third, when compared to digital approaches, how will physical physiological-based learning environments influence computational thinking, knowledge of physiological data processing concepts, and interest in STEM? The project will gather evidence from audio and video analysis, screen recordings, student work and other assessments of knowledge and interest.

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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, Bryan Y. Hernández-Cuevas and , Chris S. Crawford "MTeacher: A Gamified and Physiological-Based Web Application Designed for Machine Learning Education" Universal Access in Human-Computer Interaction. , 2023 Citation Details
Crawford, Chris S "Physiological Computing for All: Exploring Neural Interface Education" Interactions , v.32 , 2025 https://doi.org/10.1145/3704439 Citation Details
Hernández-Cuevas, Bryan Y and Lewis, Myles and Junkins, Wesley and Crawford, Chris S and Denham, Andre and Luo, Feiya "PhysioML: A Web-Based Tool for Machine Learning Education with Real-Time Physiological Data" , 2025 https://doi.org/10.1145/3641554.3701815 Citation Details
Lewis, Myles and Holloman, Amanda and Luo, Feiya and Denham, Andre and Crawford, Chris "LITI: Learning with Interactive Time Series Information" , 2023 https://doi.org/10.1109/VL-HCC57772.2023.00049 Citation Details
Lewis, Myles and Joshi, Pranay and Junkins, Wesley Cade and Ingram, Vincent and Crawford, Chris S "PhysioBots: Engaging K-12 Students with Physiological Computing and Robotics" , 2025 https://doi.org/10.1145/3706599.3720106 Citation Details

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