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2232826

Project Grant

Overview

Grant Description
SBIR Phase I: A student learning dashboard - The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in improving retention in higher education and increasing graduation rates. Currently, the average U.S. college dropout rate is 40%.

Moreover, underserved science, technology, engineering, and mathematics (STEM) student populations are more likely to leave school without a degree. Due to the COVID-19 pandemic, increased financial insecurity and mental health challenges have negatively impacted student learning.

This project aims to develop a student learning dashboard platform that acts as a co-pilot during students' higher education learning journey by delivering targeted, personalized, and real-time actionable assistance. The solution holistically identifies each student's unique learning motivation challenges (e.g., subject difficulty, relevance to career goals, social and economic constraints, etc.) and provides specific recommendations to overcome barriers.

Coaching students to learn how to learn more effectively based on their own context fosters a growth mindset, grit, and agency to help them become successful lifelong learners. The application also significantly improves diversity, equity, and inclusion in higher education, especially in STEM, and thus increases effective workforce training.

This Small Business Innovation Research (SBIR) Phase I project uses machine learning to understand each student's unique learning challenges, map how barriers affect learning motivation, and influences coursework engagement. Machine learning is applied to analyze qualitative and quantitative learning motivation and behavior data to identify gaps so real-time, targeted, and relevant guidance can be delivered while the students are still progressing through the courses rather than waiting until it might be too late for intervention.

This project provides descriptive, predictive, and prescriptive recommendations to simulate one-on-one, personalized advising at scale and at a lower cost. The technology also acts as an early detection system when students show the first sign of academic and non-academic struggles affecting their mental state of readiness to learn. When in-person human intervention is required, instructors, academic advising, and/or relevant on-campus student support services can be alerted.

This project can be used by any educational institution or private company providing in-person, flipped/hybrid, remote, synchronous, or asynchronous instruction formats. 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.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF22551
Awarding / Funding Agency
Place of Performance
Irvine, California 92602-1625 United States
Geographic Scope
Single Zip Code
Related Opportunity
22-551
Analysis Notes
Amendment Since initial award the End Date has been extended from 04/30/24 to 10/31/24 and the total obligations have increased 7% from $274,471 to $294,471.
Prenostik was awarded Project Grant 2232826 worth $294,471 from National Science Foundation in May 2023 with work to be completed primarily in Irvine California United States. The grant has a duration of 1 year 5 months and was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.

SBIR Details

Research Type
SBIR Phase I
Title
SBIR Phase I:A Student Learning Dashboard
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in improving retention in higher education and increasing graduation rates. Currently, the average U.S. college dropout rate is 40%. Moreover, underserved Science, Technology, Engineering and Mathematics (STEM) student populations are more likely to leave school without a degree. Due to the COVID-19 pandemic, increased financial insecurity and mental health challenges have negatively impacted student learning. This project aims to develop a student learning dashboard platform that acts as a co-pilot during students' higher education learning journey by delivering targeted, personalized, and real-time actionable assistance. The solution holistically identifies each student's unique learning motivation challenges (e.g., subject difficulty, relevance to career goals, social and economic constraints, etc.) and provides specific recommendations to overcome barriers. Coaching students to learn how to learn more effectively based on their own context fosters a growth mindset, grit, and agency to help them become successful lifelong learners.The application also significantly improves diversity, equity, and inclusion in higher education, especially in STEM, and thus increases effective workforce training. _x000D_ _x000D_ This Small Business Innovation Research (SBIR) Phase I project uses machine learning to understand each student's unique learning challenges, map how barriers affect learning motivation, and influences coursework engagement. Machine learning is applied to analyze qualitative and quantitative learning motivation and behavior data to identify gaps so real-time, targeted, and relevant guidance can be delivered while the students are still progressing through the courses rather than waiting until it might be too late for intervention. This project provides descriptive, predictive, and prescriptive recommendations to simulate one-on-one, personalized advising at scale and at a lower cost. The technology also acts as an early detection system when students show the first sign of academic and non-academic struggles affecting their mental state of readiness to learn. When in-person human intervention is required, instructors, academic advising, and/or relevant on-campus student support services can be alerted. This project can be used by any educational institution or private company providing in-person, flipped/hybrid, remote, synchronous, or asynchronous instruction formats._x000D_ _x000D_ 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.
Topic Code
LC
Solicitation Number
NSF 22-551

Status
(Complete)

Last Modified 4/30/24

Period of Performance
5/1/23
Start Date
10/31/24
End Date
100% Complete

Funding Split
$294.5K
Federal Obligation
$0.0
Non-Federal Obligation
$294.5K
Total Obligated
100.0% Federal Funding
0.0% Non-Federal Funding

Activity Timeline

Interactive chart of timeline of amendments to 2232826

Transaction History

Modifications to 2232826

Additional Detail

Award ID FAIN
2232826
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
DTN6VGWA9LL5
Awardee CAGE
8KTY5
Performance District
CA-47
Senators
Dianne Feinstein
Alejandro Padilla

Budget Funding

Federal Account Budget Subfunction Object Class Total Percentage
Research and Related Activities, National Science Foundation (049-0100) General science and basic research Grants, subsidies, and contributions (41.0) $274,471 100%
Modified: 4/30/24