2451599
Project Grant
Overview
Grant Description
Sttr Phase I: Design a just-in-time formative assessment algorithm for an adaptive education platform.
The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project will be achieved by developing and validating cutting-edge assessment technology to address two critical problems in math education: A) student learning deficiencies and B) teacher overload and attrition.
At the high school level, the Program for International Student Assessment (PISA) reported that the math scores of U.S. students in 2022 ranked 28th among 37 participating countries, posing substantial risk to the nation’s competitiveness in STEM fields.
This occurs while educators’ burnout and attrition is at an all-time high.
Such significant learning deficiencies in math will cost an estimated $1.1T in GDP due to the loss of workforce productivity and innovation.
Meanwhile, available tools and innovations for high school math are drastically low, in comparison to tools available for their K-8 counterparts.
In response, this STTR project will develop a web-based system providing highly efficient, personalized, formative assessments that are easily customizable by teachers themselves.
By addressing critical classroom and market needs, the project will help improve student math learning, cultivate a competitive and diverse STEM workforce, and contribute to high-tech innovation in a federal opportunity zone in the heart of the Midwest.
This Small Business Technology Transfer (STTR) Phase I project will develop a web-based formative assessment system providing highly efficient and personalized assessments that are easily customizable by teachers themselves, empowering teachers to do their work more effectively and efficiently.
Unlike traditional adaptive assessment systems that often reduce the teacher’s role, this platform leverages cognitive diagnostic modeling to identify students’ strengths and weaknesses in high school math in real time, both individually and collectively.
An innovative machine learning algorithm clusters students for targeted instruction based on their mathematical competencies and current understanding, while also tracking their progress to enable timely interventions.
Teachers can regularly and flexibly regroup students based on updated assessments, ensuring that instruction remains tailored to each class’s needs.
Additionally, advancements in large language models (LLMs) will be utilized to expand the item bank, supporting the platform’s scalability and meeting ongoing assessment demands in diverse classroom environments.
The system’s usability and effectiveness will be validated through a comprehensive pilot study, demonstrating its potential to enhance educational outcomes and streamline teaching processes.
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.
Subawards are planned for this award.
The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project will be achieved by developing and validating cutting-edge assessment technology to address two critical problems in math education: A) student learning deficiencies and B) teacher overload and attrition.
At the high school level, the Program for International Student Assessment (PISA) reported that the math scores of U.S. students in 2022 ranked 28th among 37 participating countries, posing substantial risk to the nation’s competitiveness in STEM fields.
This occurs while educators’ burnout and attrition is at an all-time high.
Such significant learning deficiencies in math will cost an estimated $1.1T in GDP due to the loss of workforce productivity and innovation.
Meanwhile, available tools and innovations for high school math are drastically low, in comparison to tools available for their K-8 counterparts.
In response, this STTR project will develop a web-based system providing highly efficient, personalized, formative assessments that are easily customizable by teachers themselves.
By addressing critical classroom and market needs, the project will help improve student math learning, cultivate a competitive and diverse STEM workforce, and contribute to high-tech innovation in a federal opportunity zone in the heart of the Midwest.
This Small Business Technology Transfer (STTR) Phase I project will develop a web-based formative assessment system providing highly efficient and personalized assessments that are easily customizable by teachers themselves, empowering teachers to do their work more effectively and efficiently.
Unlike traditional adaptive assessment systems that often reduce the teacher’s role, this platform leverages cognitive diagnostic modeling to identify students’ strengths and weaknesses in high school math in real time, both individually and collectively.
An innovative machine learning algorithm clusters students for targeted instruction based on their mathematical competencies and current understanding, while also tracking their progress to enable timely interventions.
Teachers can regularly and flexibly regroup students based on updated assessments, ensuring that instruction remains tailored to each class’s needs.
Additionally, advancements in large language models (LLMs) will be utilized to expand the item bank, supporting the platform’s scalability and meeting ongoing assessment demands in diverse classroom environments.
The system’s usability and effectiveness will be validated through a comprehensive pilot study, demonstrating its potential to enhance educational outcomes and streamline teaching processes.
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.
Subawards are planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH / SMALL BUSINESS TECHNOLOGY TRANSFER PHASE I PROGRAMS", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF24579
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
South Bend,
Indiana
46617-1364
United States
Geographic Scope
Single Zip Code
Nd-Diagenomix was awarded
Project Grant 2451599
worth $305,000
from National Science Foundation in February 2025 with work to be completed primarily in South Bend Indiana United States.
The grant
has a duration of 1 year 2 months and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
STTR Phase I
Title
STTR Phase I: Design a Just-In-Time Formative Assessment Algorithm for an Adaptive Education Platform
Abstract
The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project will be achieved by developing and validating cutting edge assessment technology to address two critical problems in math education: a) student learning deficiencies and b) teacher overload and attrition. At the high school level, the Program for International Student Assessment (PISA) reported that the math scores of U.S. students in 2022 ranked 28th among 37 participating countries, posing substantial risk to the nation’s competitiveness in STEM fields. This occurs while educators’ burnout and attrition is at an all time high. Such significant learning deficiencies in math will cost an estimated $1.1T in GDP due to the loss of workforce productivity and innovation. Meanwhile, available tools and innovations for high school math are drastically low, in comparison to tools available for their K-8 counterparts. In response, this STTR project will develop a web-based system providing highly efficient, personalized, formative assessments that are easily customizable by teachers themselves. By addressing critical classroom and market needs, the project will help improve student math learning, cultivate a competitive and diverse STEM workforce, and contribute to high-tech innovation in a Federal Opportunity Zone in the heart of Midwest.
This Small Business Technology Transfer (STTR) Phase I project will develop a web-based formative assessment system providing highly efficient and personalized assessments that are easily customizable by teachers themselves, empowering teachers to do their work more effectively and efficiently. Unlike traditional adaptive assessment systems that often reduce the teacher’s role, this platform leverages cognitive diagnostic modeling to identify students’ strengths and weaknesses in high school math in real time, both individually and collectively. An innovative machine learning algorithm clusters students for targeted instruction based on thei
Topic Code
LC
Solicitation Number
NSF 24-579
Status
(Ongoing)
Last Modified 2/5/25
Period of Performance
2/1/25
Start Date
4/30/26
End Date
Funding Split
$305.0K
Federal Obligation
$0.0
Non-Federal Obligation
$305.0K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2451599
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
J4FBXN13NLQ3
Awardee CAGE
9H6J4
Performance District
IN-02
Senators
Todd Young
Mike Braun
Mike Braun
Modified: 2/5/25