Big Data and the Predictive Analytics Reporting (PAR) Framework (Panel Presentation)

• In Event: Online Teaching and Learning SIG Business Meeting

Abstract

Big Data and the Predictive Analytics Reporting (PAR) Framework (Panel Presentation)
Funded by the Gates Foundation and managed by WCET, the Predictive Analytics Reporting (PAR) Framework is a longitudinal data-mining project whose purpose is to federate student records across multiple, diverse institutions of higher education. The PAR Framework makes it possible to conduct predictive analyses on a massive collection of student records in a quest to better understand the variables affecting student retention and progression to degree in online programs. The PAR Framework is unique in its decision to employ predictive techniques commonly found in business intelligence settings to aid in educational decision-making, and in that it has been driven by the collaborative efforts of six very different partner institutions -- American Public University System, the Colorado Community College System, Rio Salado College, the University of Hawaii System, University of Illinois – Springfield, and the University of Phoenix – to normalize institutional data around common data definitions.
Panelists will include Phil Ice (American Public University System), the PAR Principal Investigator; Karen Swan (University of Illinois - Springfield), local project director; Peter Shea (University at Albany) and Ben Arbaugh (University of Wisconsin – Oshkosh), project evaluators, and Rob Mitchell (American Public University System), PAR data analyst. Panelists will discuss the work of the PAR project, its initial findings, and the implications and efficacy of big data approaches to educational research.

Authors

• Karen P. Swan, University of Illinois at Springfield

• Peter Shea, University at Albany - SUNY

• J. B. Arbaugh, University of Wisconsin - Oshkosh

• Rob Mitchell, American Public University System