This MOOC offers a flexible, collaborative introduction to learning analytics in higher education. You’ll learn by doing, using realistic data and code. Everyone involved in higher education has questions. Students want to know how they’re doing and which classes they should take.
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Faculty members want to understand their students’ backgrounds and to learn whether their teaching techniques are effective. Staff members want to be sure the advice they provide is appropriate and find out whether college requirements accomplish their goals. Administrators want to explore how all of their students and faculty are doing and to anticipate emerging changes. The public wants to know what happens in college and why.
Everyone has questions. We have the chance to help them find answers.
Practical Learning Analytics has a specific goal: to help us collectively ponder learning analytics in a concrete way. To keep it practical, we will focus on using traditional student record data, the kinds of data every campus already has. To make it interesting, we will address questions raised by an array of different stakeholders, including campus leaders, faculty, staff, and especially students. To provide analytic teeth, each analysis we discuss will be supported by both realistic data and sample code.
What you'll learn:
- About the landscape of learning analytics in higher education
- How to bring in data of your own for analysis and visualization
- About performance prediction in a course: up to and including grade penalties, placement analyses, performance disparities and their correlates, course-to-course correlation
- How institutions are creating early warning systems and personalized communication
- How to apply learning analytics to observe differences and probe impact, capturing more and better information
