- 3 Sequences
- Intermediate Level
- Starts on August 12, 2018
- Ends on April 13, 2019
Predictive Modeling in Learning Analytics
Course details
Syllabus
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
Prerequisite
This course is intended for those who have a bachelor’s degree and are interested in developing learning and data science skills for employment in education, corporate, nonprofit, and military sectors. Experience with programming and statistics will be beneficial to participants.
Instructors
Christopher Brooks
Research Assistant Professor, School of Information, University of Michigan
University of Michigan
Craig Thompson
Learning Analytics Research Analyst at the Centre for Teaching, Learning and Technology
University of British Columbia
Editor
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