LEARNING ANALYTICS THEORY AND PRACTICE
Application of data mining techniques to large educational datasets to gain important insights into how people learn. Examines the spectrum of prevalent learning analytics methods and applications, from institutional effectiveness, to classroom-level interventions, to standardized assessments, and beyond.
Prerequisites & course information
Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Identify the fundamentals of machine learning and database interventions Audience: Graduate 2. Analyze data ethics, regulations, and privacy issues Audience: Graduate 3. Utilize prediction methods Audience: Graduate 4. Engage in structure discovery Audience: Graduate 5. Demonstrate understanding of relationship mining Audience: Graduate Learning Outcomes: 1. Identify the fundamentals of machine learning and database interventions Audience: Graduate 2. Analyze data ethics, regulations, and privacy issues Audience: Graduate 3. Utilize prediction methods Audience: Graduate 4. Engage in structure discovery Audience: Graduate 5. Demonstrate understanding of relationship mining Audience: Graduate
Sources
- Course catalogue ↗2026-2027