The PAR Framework Proof of Concept: Initial Findings from a Multi-Institutional Analysis of Federated Postsecondary Data

Philip Ice, Sebastián Díaz, Karen Swan, Melissa Burgess, Mike Sharkey, Jonathan Sherrill, Daniel R. Huston, Hae Okimoto

Abstract


Despite high enrollment numbers, postsecondary completion rates have generally remained unchanged for the past 30 years and half of these students do not attain a degree within six years of initial enrollment. Although online learning has provided students with a convenient alternative to face-to-face instruction, there remain significant questions regarding online learning program quality, particularly when considering patterns of student retention and progression. By aggregating student and course data into one dataset, six postsecondary institutions worked together toward determining factors that contribute to retention, progression, and completion of online learners with specific purposes: (1) to reach consensus on a common set of variables among the six institutions that inform student retention, progression and completion; (2) to explore advantages and/or disadvantages of particular statistical and methodological approaches to assessing factors related to retention, progression and completion. In the relatively short timeframe of the study, 33 convenience variables informing retention, progression, and completion were identified and defined by the six participating institutions. This initiative, named the Predictive Analytics Reporting Framework (PAR) and the initial statistical analyses utilized are described in this paper.

Keywords


retention, progression, completion, online learning, postsecondary, predictive analytics, data repositories