Learning Analytics: Using Big Data to Predict Student Success

Published:
Presentations and Seminars
Author(s) and Contributors:
Speaker(s): Sebastian Diaz Hae Okimoto
Source(s) and Collection(s):
Presentation(s): ELI Webinars Webinars
ParentTopics:
Analytics Big Data Learning Analytics Online Learning Student Learning Support Student Retention Student Success

Abstract

Join Malcolm Brown, EDUCAUSE Learning Initiative director, and Veronica Diaz, ELI associate director, as they moderate this webinar with Sebastian Diaz and Hae Okimoto on a multi-institutional proof-of-concept project on looking at data of online learning to predict retention, progression, and completion. Despite increasing enrollments in postsecondary institutions, 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 access for students, as well as a convenient alternative to face-to-face instruction, this innovative platform for learning is similarly laden with retention-related concerns.

This webinar will describe how 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, and; (2) to discover advantages and/or disadvantages to particular statistical and methodological approaches to predicting factors related to retention, progression and completion. In the relatively short timeframe of the study, approximately 30 convenience variables informing retention, progression, and completion were identified and defined by the six participating institutions. Statistical analyses explored the associations among variables and as predictors for academic progression. In addition to the statistical results obtained, the project revealed insights into organizational challenges inherent in any study involving multiple institutions and their respective data.

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