Earlier this year, a bipartisan bill was put forward in Congress to fix one of the biggest impediments preventing hopeful American high school students from receiving an affordable higher education: the Free Application for Federal Student Aid, otherwise known as FAFSA.
Although it sounds ludicrous, the 10-page, 100 question document, which students (or, more likely, their families) are expected to complete in order to receive government-backed college grants or qualify for loans, has become so burdensome that some 2 million students who would have qualified for Pell grants in 2011 and 2012 did not file a FAFSA form, according to voxgov.com.
As a recent New York Times piece on the ridiculous financial aid form put it, “The Fafsa burdens families and prevents students from attending college, while doing little to target federal aid.” The bill, sponsored by Sen. Lamar Alexander (R-Tenn.) and Sen. Michael Bennet (D-Colo.), would reduce the complicated form down to two questions, a move that would likely lead to more applicants as well as more grants and loans being distributed.
However, purely bureaucratic means aren’t the only way that some are trying to make an affordable college education more accessible. Kenny Sandorffy and his team at ScholarshipOwl are trying to leverage big data and machine learning to make students more aware of the scholarships that they may qualify for, but don’t even know about.
The Tel Aviv-founded company, which has its U.S. headquarters in Santa Monica, has simplified what was almost an impossible task for individuals looking to find ways to make higher education — which now has an average price tag of about $30,000 per year. It takes a few data points — questions answered by students looking for scholarships — and then matches its users with the more than 3.5 million possible scholarships that they may qualify for.
As Sandorffy told me, as users add more information to their profile, the potential scholarships that may be available to them become more personalized and expansive.
What’s more telling is that ScholarshipOwl also streamlines the application process for most of the scholarships it presents to qualifying students on its site. (Some scholarships that require specific essays as part of the application process can also be completed through the site.)
Additionally, the company is hoping to expand upon its existing features by implementing an automatically recurring process for applications that need to be renewed. As Sandorffy explained, some scholarships can be reapplied for every month, which many applicants do not know, so ScholarshipOwl will complete the renewal process for its users to get them more tuition savings.
Sandorffy took up the crusade to find a better way to discover and apply for scholarships after his own personal frustrations with the process. Looking into the problem led him to start a blog that covered different types solutions available for finding ways to lower the high cost of college education; as part of the blog project, Sandorffy would write about scholarships and grants that many students weren’t aware of as well as tips and tricks for applying.
“One thing led to another, and while writing and researching about scholarships, I started to wonder why there wasn’t a process to make it easier for students to get scholarships,” Sandorffy said.
“Why don’t more students have access to the $16 billion that is given out every year in the U.S. for scholarships and grants, especially the one’s who need it?” he said. “I’ve come to realize that the inequality comes from a lack of connection. Students who need scholarships the most are the one’s who have the least amount of time to complete the process.”
So ScholarshipOwl was created to automate the entire process to solve the problem.
“At the end of the day, we want more students applying and getting scholarships,” Sandorffy said.
ScholarshipOwl uses big data, machine learning to fix the convoluted scholarship application process originally published by Gigaom, © copyright 2015.
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