Tackling the under-matching problem with machine learning
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The Problem


Each year, more than 400,000 qualified low-income students do not attend college, and another 200,000 are significantly undermatched. Undermatched students are those who could be attending more rigorous schools based on their qualifications. These undermatched students attend colleges that don’t provide them with the right environment for success, which affects their long-term earnings and social mobility, continuing the cycle of poverty.

How we help


First-generation college applicants are often under-supported when it comes to the difficult decision of choosing a school.

First-generation college students are often under-supported when it comes to the difficult decision of choosing a school.

They may make decisions based on criteria such as location, or where their friends are going. This leads to students matriculating at schools that do not live up to their potential, and often increases their likelihood of dropping out. Uptake gives low-income and first-generation college applicants the power of informed choice. They can choose schools that help them reach their full potential, and provide the support they need to graduate.

Student Union helps connect first-generation college applicants with a portfolio of schools that provide them with the best chances of admission and graduation.

Student Union

Students enter their GPA, test scores, and demographic information.

Student Union

Student Union finds school choices that are best matches based on school graduation rates.

Student Union

Students can filter by factors such as major, location, diversity, and financial burden.

Advantages of Student Union

Based on previous successes

Many matching tools rely on rules-based models to place students into selectivity buckets. Our algorithms analyze correlations found in historical data from first-generation college students to generate predictions for each student’s best fit schools.


Tailors to each student

We focus on first-generation applicants and students from low-income areas. Colleges are suggested based on where students with similar profiles have been admitted and have succeeded in graduating.


Improves continuously

Our models continuously improve through machine learning. The more data the models ingest, the more patterns and nuances they learn to recognize. The more the students use it, the better the app becomes at predicting success.


Shares knowledge

Because our tool is used by multiple organizations we can better predict acceptance, persistence, and graduation than any one organization can by itself.



Goal: Number of students to use our app over the next 18-24 months

End of 2018 Goals

  • Help increase students’ chances of graduating college by providing the tools to choose the schools they will most likely succeed at.
  • Bring together leading college success nonprofits and school networks to attract over 100,000 students to the app.

To reach these goals, we need a wealth of partners to adopt and distribute Student Union to their stakeholders.

Become a Partner

We need partners who are able to engage high school students with Student Union, or who want to help us further the project.

We are looking for additional partners, including:

  • Nonprofits with a mission of helping first-generation college students graduate from college
  • Philanthropic foundations with missions to uplift first-generation college students
  • School networks who can spread awareness to high schoolers
  • Examination organizations who reach millions of students
  • Other tech companies who want to help launch this project
  • Government institutions who want to contribute data or help scale the product
  • Research institutions with extensive knowledge who want to support the project