2232502
Project Grant
Overview
Grant Description
Sbir Phase I: A novel method to scaling mentoring and career development in institutes of higher education - The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to increase post-secondary student success via academic and career guidance.
A large body of research on career navigation has studied how post-secondary education, career readiness (understanding viable career paths at graduation), and its interconnectedness are important for a growing number of first-generation, low-income, and underrepresented students.
With increasing undergraduate degree program offerings in response to an evolving future of work and student-to-counselor ratios of 1:1,800 in public colleges, career guidance and academic navigation risk being unavailable.
As a result, during the pandemic, institutes of higher education (IHEs) that serve students of color and students from low-income backgrounds saw declines in enrollment that far outpaced their predominantly white peer institutions.
The proposed platform intends to increase the visibility, accessibility, and discoverability of competencies to potential career and academic paths for students at IHEs.
The platform envisions doing this via near-peer role models who are similar in their dimensions of self-efficacy.
With more than 85 million jobs that could go unfilled by 2030, the proposed platform may help alleviate part of that shortage by widening the talent aperture.
The intellectual merit of this project is in the company's patented technology of a unified, multi-dimensional data representation model that creates a "competency fingerprint" for each user.
The data representation method enables better machine learning models to "infer" competency from unstructured data of a student's traditional and non-traditional learning experience, rather than degrees, majors, grade point averages (GPAs), or test scores.
The platform uses a consistent, scalable competency nomenclature for hard and soft skills gained via traditional academic and outside-of-the-classroom experiences to discover academic-career paths where the students' learning competencies may be in demand.
There is a significant technical challenge in adopting this technology for the inter/cross-disciplinary jobs of the future: such a platform requires a robust, larger data set to evaluate the relevance of matching, in a discipline-agnostic context.
Reduction of this variability is the key technical risk to be overcome by the proposed research and development.
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.
A large body of research on career navigation has studied how post-secondary education, career readiness (understanding viable career paths at graduation), and its interconnectedness are important for a growing number of first-generation, low-income, and underrepresented students.
With increasing undergraduate degree program offerings in response to an evolving future of work and student-to-counselor ratios of 1:1,800 in public colleges, career guidance and academic navigation risk being unavailable.
As a result, during the pandemic, institutes of higher education (IHEs) that serve students of color and students from low-income backgrounds saw declines in enrollment that far outpaced their predominantly white peer institutions.
The proposed platform intends to increase the visibility, accessibility, and discoverability of competencies to potential career and academic paths for students at IHEs.
The platform envisions doing this via near-peer role models who are similar in their dimensions of self-efficacy.
With more than 85 million jobs that could go unfilled by 2030, the proposed platform may help alleviate part of that shortage by widening the talent aperture.
The intellectual merit of this project is in the company's patented technology of a unified, multi-dimensional data representation model that creates a "competency fingerprint" for each user.
The data representation method enables better machine learning models to "infer" competency from unstructured data of a student's traditional and non-traditional learning experience, rather than degrees, majors, grade point averages (GPAs), or test scores.
The platform uses a consistent, scalable competency nomenclature for hard and soft skills gained via traditional academic and outside-of-the-classroom experiences to discover academic-career paths where the students' learning competencies may be in demand.
There is a significant technical challenge in adopting this technology for the inter/cross-disciplinary jobs of the future: such a platform requires a robust, larger data set to evaluate the relevance of matching, in a discipline-agnostic context.
Reduction of this variability is the key technical risk to be overcome by the proposed research and development.
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
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Fremont,
California
94539-3011
United States
Geographic Scope
Single Zip Code
Related Opportunity
22-551
Analysis Notes
Amendment Since initial award the total obligations have decreased 46% from $550,000 to $295,000.
Epixego was awarded
Project Grant 2232502
worth $295,000
from National Science Foundation in April 2023 with work to be completed primarily in Fremont California United States.
The grant
has a duration of 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 novel method to scaling mentoring and career development in Institutes of Higher Education
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to increase post-secondary student success via academic and career guidance. A large body of research on career navigation has studied how post-secondary education, career readiness (understanding viable career paths at graduation), and its interconnectedness are important for a growing number of first-generation, low-income, and underrepresented students. With increasing undergraduate degree program offerings in response to an evolving future of work and student-to-counselor ratios of 1: 1,800 in public colleges, career guidance, and academic navigation risk being unavailable. As a result, during the pandemic, Institutes of Higher Education (IHEs) that serve students of color and students from low-income backgrounds saw declines in enrollment that far outpaced their predominantly White peer institutions. The proposed platform intends to increase the visibility, accessibility, and discoverability of competencies to potential career and academic paths for students at IHEs. The platform envisions doing this via near-peer role models who are similar in their dimensions of self-efficacy. With more than 85 million jobs that could go unfilled by 2030, the proposed platform may help alleviate part of that shortage by widening the talent aperture._x000D_ _x000D_ The intellectual merit of this project is in the company’s patented technology of a unified, multi-dimensional, data representation model that creates a ‘competency fingerprint’ for each user. The data representation method enables better machine learning models to ‘infer’ competency from unstructured data of a student’s traditional and non-traditional learning experience, rather than degrees, majors, grade point averages (GPAs), or test scores. The platform uses a consistent, scalable, competency nomenclature for hard and soft skills gained via traditional academic and outside-of-the-classroom experiences to discover academic-career paths where the students’ learning competencies may be in demand. There is a significant technical challenge in adopting this technology for the inter/cross-disciplinary jobs of the future:such a platform requires a robust, larger data set to evaluate the relevance of matching, in a discipline-agnostic context. Reduction of this variability is the key technical risk to be overcome by the proposed research and development._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 9/5/23
Period of Performance
4/1/23
Start Date
9/30/23
End Date
Funding Split
$295.0K
Federal Obligation
$0.0
Non-Federal Obligation
$295.0K
Total Obligated
Activity Timeline
Transaction History
Modifications to 2232502
Additional Detail
Award ID FAIN
2232502
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
Z5HNA72LPLA7
Awardee CAGE
873Y7
Performance District
CA-14
Senators
Dianne Feinstein
Alejandro Padilla
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) | $295,000 | 100% |
Modified: 9/5/23