Paths to Predictive Learning Analytics: Can Data Predict Learner Success?

Wednesday, November 07 | 3:15PM–4:00PM ET | November 7, 2018
Session Type: Virtual
We will describe Indiana University's journey in developing predictive models of learners' interactions with digital learning management environments (LMEs). Through partnerships with leaders in modeling and predicting learner outcomes from LME interactions, we will discuss the lessons learned, the issues encountered, and the relevance of predictive analytics in higher education.

Outcomes: Identify relevant data architectures and pipelines for the development of predictive models * Understand the process of developing and implementing predictive models from digital LMEs and institutional systems * Explore the issues and potential for the application of predictive models in higher education


  • Matthew Gunkel

    Associate Vice Chancellor and CIO, University of California, Riverside
  • Ben Motz

    Assistant Professor, Indiana University

Resources & Downloads

  • Slides

    1 MB, pdf - Updated on 1/27/2024
  • Transcript

    44 KB, docx - Updated on 1/27/2024
  • Recording

    Updated on 1/27/2024