Leveraging Amazon’s infrastructure
PAR Framework originally began as a Gates Foundation-funded project in 2011, with support from the Amazon Web Services (AWS) Education Grants Program.
AWS in Education allows educators and academic researchers to apply to obtain free usage credits to tap into the on-demand infrastructure of the Amazon Web Services cloud.
Often, large research projects require extensive compute power and storage infrastructure to complete. Instead of purchasing a large amount of hardware, researchers can get started by opening an AWS account, eliminating much of the heavy lifting of provisioning and configuring open-source software frameworks for processing very large data sets.
“We needed to build a data warehouse able to analyze and compare numerous, diverse data sets,” Davis explained. “We require a scalable backend, able to grow and adapt, as well as provide high-performance access. We also needed an infrastructure that’s as security-conservative as higher-ed institutions. Really, we needed best of breed.”
After PAR secured a hosting grant during their research phase, when PAR was ready to emerge into its current operations as an independent 501c3 in January 2015, AWS provided the flexibility that allowed the non-profit to shift all the existing infrastructure to its private accounts, maintaining the infrastructure’s scalability and security. Operations went on uninterrupted for PAR and its member institutions.
Protecting student privacy
PAR gathers a rich set of student-level course and demographic data from members, which is then used to develop institutional-, program-, course- and student-level descriptive analytics and predictive insights. The PAR ecosystem of reports, predictive models, tools and frameworks is geared at improving student success, retention and credential completion.
Therefore, said Davis, preparing and securely transmitting anonymized student-level data is a key part of the process.
“Institutional partners gather data from their local systems according to the PAR Framework common data definitions and a detailed file specification,” Davis explained. “As a last step before data submission, they remove any personally identifiable data, including date of birth, social security number and local student ID number and replace those items with a PAR student ID. Institutions maintain a translation table of their internal ID to PAR Student ID which will be used to easily re-identify those students after the data has been analyzed by PAR. PAR never receives, or has access to, student-level identifiable data, adding a layer of privacy and security for student records.”
Why PAR works
1. The first way PAR helps institutions is through its AWS-hosted Student Success Matrix (SSMx), which gives institutions a means to inventory, organize, and conceptualize interventions aimed at improving student outcomes.
“Often there’s no single version of the truth about student success programs on a campus, since issues with student retention could come down to multiple departments or programs,” said Davis. “Until an institution has a comprehensive picture of campus intervention systems and student success programs, it’s hard to know what to leverage.”
According to Davis, the SSMx essentially inventories and categorizes all interventions on campus, with unlimited access to faculty, staff, and administration to interact with the intervention data.
“This allows a campus to quickly identify overlaps and gaps,” she noted.
PAR also creates “Student Watch List” reports, generated from predictive models custom to each member institution. The watch lists evaluate every student in the institutional data set, assigning risk scores, and revealing the individual student characteristics that contribute to risk of the student not passing key Obstacle Courses [courses with high drop-out or fail rates] or not being retained at the institution. These watch lists can be downloaded by members, and using translation tables, PAR members can restore the identities of the students in the watch list, enabling the institution to take action on the insights revealed through predictive modeling while also not putting student privacy at risk.
(Next page: Closing the loop; data for non-traditional learners)
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