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

Thursday, November 02 | 12:15PM–1:15PM ET | Exhibit Hall A-C, 200 Level
Session Type: Poster Session
Delivery Format: Poster Session
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


  • Matt Bernacki

    Assistant Professor of Educational Psychology, University of Nevada, Las Vegas
  • Cam Johnson

    Director of IT Operations, University of Nevada, Las Vegas

Resources & Downloads

  • educause 102117 Institutional Intelligence PRINT

    924 KB, pdf - Updated on 1/26/2024
  • Johnson Bernacki UNLV 2017 Increasing Student Success Completion and Retention through Machine Data and Predictive Analytics

    3 MB, pdf - Updated on 1/26/2024