Increasing Student Success, Completion, and Retention through Machine Data and Predictive Analytics

Increasing student achievement and retention are critical focus areas at education institutions across the nation and around the globe. This interactive session will demonstrate the UNLV's innovative use of machine data and predictive analytics to improve student performance and identify at-risk students.

Outcomes: Understand how existing data can describe student learning and inform faculty and students * Propose your own data models to predict student achievement using learning analytics programs * Learn to develop early warning/learning strategy programs for at-risk students