Current Issues in Emerging eLearning

Article Title

Adaptive Analytics: It’s About Time

Authors

Charles Dziuban, University of Central FloridaFollow
Colm Howlin, RealizeitFollow
Patsy Moskal, University of Central FloridaFollow
Tammy Muhs, University of Central FloridaFollow
Connie Johnson, Colorado Technical UniversityFollow
Rachel Griffin, University of Central FloridaFollow
Carissa Hamilton, University of Central FloridaFollow

Abstract

This article describes a cooperative research partnership among a large public university, a for-profit private institution and their common adaptive learning platform provider. The focus of this work explored adaptive analytics that uses data the investigators describe as metaphorical “digital learning dust” produced by the platform as a matter of course. The information configured itself into acquired knowledge, growth, baseline status and engagement. Two complimentary models evolved. The first, in the public university, captured end-of-course data for predicting success. The second approach, in the private university, formed the basis of a dynamic real-time data analytic algorithm. In both cases the variables that best predicted students at risk (effective use of time and revision attempts) were deemed teachable skills that can improve with intervention.

Keywords

Adaptive Learning, Predictive Analytics, Higher Education, Cooperative Research

Recommended Citation

Dziuban, Charles; Howlin, Colm; Moskal, Patsy; Muhs, Tammy; Johnson, Connie; Griffin, Rachel; and Hamilton, Carissa (2020) "Adaptive Analytics: It’s About Time," Current Issues in Emerging eLearning: Vol. 7: Iss. 1, Article 4.
Available at: https://scholarworks.umb.edu/ciee/vol7/iss1/4