PAR Framework Releases Full Data Definitions

The Predictive Analytics Reporting (PAR) Framework project has publicly released the full definitions for the data included in the PAR database to encourage distribution of the definitions in the higher education research community.

The PAR Framework is a longitudinal, multi-institutional data-mining project managed by the WICHE Cooperative for Educational Technologies (WCET). According to information on the organization's site, "the PAR Framework makes it possible to create meaningful benchmark reports, conduct predictive analyses, and explore best practices in student retention on a massive collection of student records in a quest to better understand the variables affecting student loss."

The data includes more than 60 institutional, transcript, and student-level variables. "These variables will be used as building blocks in various strategic combinations to craft meaningful outcome measures and actionable predictors of student risk which further highlights the importance of common definitions," said Beth Davis, PAR Framework project director, in a prepared statement.

The PAR Framework has published the data definitions using the Data Cookbook, a collaborative data dictionary and data management tool built for higher education by IData. The definitions are published under a Creative Commons license with attribution, non-commerical, share-alike conditions.

Until now, the data fields and definitions used in the PAR Framework have been available only to the organization's 16 institutional partners, who have submitted more than 1.7 million anonymized and institutionally de-identified student records and 8.1 million course-level records to the dataset.

"The online Data Cookbook forum for publicly sharing our data model creates opportunities for alignment and collaboration with other projects and other datasets meaningful in higher education," said Mike Sharkey, director of academic analytics at the Apollo Group (parent company of the University of Phoenix), in a prepared statement.

The PAR Framework plans to continue to refine its data set to align with the recently released Common Education Data Standards (CEDS) version 3 and other higher education data sets.

Further information about the PAR Framework's data definitions can be found on the Data Cookbook site.

About the Author

Leila Meyer is a technology writer based in British Columbia. She can be reached at [email protected].

Featured

  • layered glass panels and light trails

    Stanford Online Launches Immersive Learning Studio

    Stanford Online recently marked its 30th anniversary with the announcement of a new immersive learning studio, according to a university news release. The studio takes advantage of AI-powered and immersive learning technologies to continue delivering personalized and faculty-led education.

  • artificial intelligence on laptop

    OpenAI to Combine AI Products into Desktop 'Superapp'

    OpenAI is reportedly developing a desktop application that would combine several of its emerging AI products into a single platform, according to reports, marking the latest step in the company's effort to transform ChatGPT from a standalone chatbot into a broader productivity and automation environment.

  • Abstract futuristic digital network with glowing padlock icons

    Microsoft Intros New Agentic AI Security Multi-Model Defense System

    A new multi-model agentic AI security system built by Microsoft's Autonomous Code Security team helped researchers find 16 new vulnerabilities across the Windows networking and authentication stack, the company anounced in a recent security blog post.

  • Jason Palm

    AI, Identity, and Speed: Cybersecurity Priorities for Higher Ed

    Fortinet Security Operations Specialist Jason Palm explains how AI is raising new security challenges for higher education, requiring stronger governance, identity protection, threat detection, automation, and incident readiness.