$1M NSF Award for Enhancing Educational Research With AI and Cloud Infrastructure Training
ECE Associate Professor Ningfang Mi is leading a multi-institution, $1 million NSF grant to create tools, resources, and templates to enable educators to analyze student data and identify trends more effectively to better determine improvements to curriculum and testing.
Ningfang Mi, associate professor of electrical and computer engineering, will lead a $1 million National Science Foundation grant that will include researchers from Temple University, George Mason University, and University of North Carolina at Charlotte to provide resources to K-12 school districts that would enable them to efficiently analyze student data, identify trends, and more effectively implement improvements to curriculum and testing.
The project, “AI4EDU: Cloud Infrastructure-Enabled Training for AI in Educational Research and Assessment,” will also lay the groundwork for eventually creating a public data repository containing tools, templates, and resources for educators.
“This is not the classic computer-related research,” Mi says. “This is bringing together computer engineering and education research to provide training and resources.”
In many current education systems, rich data sets exist, but the tools to efficiently extract meaning from them are not widely adopted by education researchers, says Mi. Using the tools currently in development, educators will be able to identify trends more quickly in student testing and learning, which will enable them to make adjustments to such core educational elements as curriculum and student assessments.
Because privacy is a key concern within education systems, distributing computing and federated learning approaches will be applied, enabling models to be built on a global level without sharing an individual school’s raw data.
“We want to help education researchers and analysts get faster results by learning AI tools,” Mi says. “Before, they would perhaps use a statistical analysis application, but we want to help them learn more from these rich data sets or even combine data sets to identify new information.”
Abstract Source: NSF
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