Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.


Predictive Modeling in Learning Analytics

University of Texas Arlington via edX

This course may be unavailable.


This course will introduce you to the tools and techniques of predictive models as used by researchers in the fields of learning analytics and educational data mining. It will cover the concepts and techniques that underlie current educational “student success” and “early warning” systems, giving you insight into how learners are categorized as at-risk through automated processes.

You will gain hands-on experience building these kinds of predictive models using the popular (and free) Weka software package. Also, included in this course is a discussion of supervised machine learning techniques, feature selection, model fit, and evaluation of data based on student attributes. Throughout the course, the ethical and administrative considerations of educational predictive models will be addressed.


Week 1: Prediction

  • Predictive models vs. explanatory models
  • The predictive modeling lifecycle
  • Predictive models of student success
  • Ethical considerations with predictive models
  • Overview of the state of the practice in educational predictive models

Week 2: Supervised Learning

  • Supervised machine learning techniques, including Decision Trees and Naive Bayes

Week 3: Model Evaluation

  • Making predictions
  • Model evaluation and comparison
  • Practical considerations

Taught by

Christopher Brooks and Craig Thompson


Start your review of Predictive Modeling in Learning Analytics

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.