Use Student Data To Streamline The College Application Process
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This post is a response to RFI 2014-08649.
Document Citation: 79 FR 21449 Page: 21449 -21453 (5 pages) Agency/Docket Number: Docket ID ED-2014-OUS-0040 Document Number: 2014-08649
Elements of the college application process are repetitive, and require students to pull and collate data already collected elsewhere. For some students, family circumstances can create barriers to getting this information together. Additionally, some students will be navigating the process on their own. To support students looking to transition into college, we should use existing systems to support them along that path.
FAFSA and the Common Application could be streamlined to make use of data already collected, by the Internal Revenue Service and state longitudinal data systems, respectively.
FAFSA already connects with IRS data. Information about this connection is available at . However, this connection needs to be updated to support more types of families (or, more types of tax filing, as needed by different family structures). Right now, only a subsection of students can be supported with the FAFSA data transfer tool. While this is a good start, improving and expanding the API used for data transfer from the IRS to the FAFSA should be prioritized. Additionally, the FAFSA data transfer tool appears to only be used for populating the FAFSA form - we will adress other possible supports later in this writeup.
Transitioning to the Common Appication, much of the information required for the Common Application is already stored in state level longitudinal datastores. Over the course of a student's K12 education, the information required by the Common Application has been collected and stored. Given that these datastores implement a version of CEDS, or SIF, or both, we can assume that the data coming from these systems is usable. The Common Application requires information about a student's academic history, demographics, tests, etc, that are all available via data stored in the state-level longitudinal data store. Additionally, some students qualify for fee waivers for the common application. Based on data obtained for the FAFSA, students who are eligible to have their fees waived could be identified proactively.
As students go through college, their loan obligations could be tracked against their income, and their family income. This data could be tracked against other factors (geography, school, economic conditions, family income) to help flag students who might experience difficulties staying current in paying off their student loans. Data could be used to identify students in need of support before they fell behind on their loans (using anything between loan consolidation, deferment, and - in some cases - forgiveness). Programs to support students already exist; data could be used to connect students with support in more timely and efficient ways.
Obviously, there are privacy concerns that need to be addressed as part of the implementation. As a start, connecting different data sources should only happen if a student initiates the process. Students need to own and control where their information goes. Additionally, any implementation needs to allow students to override data that is auto-completed, as machines often make mistakes that require human intervention to fix. But, given that much of the data required to apply to college is already collected and organized, we should not require students to do unnecessary repetition as part of the college application process.
