Everyone in education has questions – Practical Learning Analytics is about answering them. To be practical, we’ll focus on data every university records; to keep things interesting, we’ll examine questions raised by many audiences; to ensure impact, we’ll provide realistic data and example code.
Everyone involved in higher education has questions. Students want to know how they’re doing and which classes they should take. 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.
Learning analytics is about using data to improve teaching and learning. You might wonder why there’s suddenly so much conversation about this previously invisible topic. After all, institutions of higher education have maintained careful records of student progress and outcomes for more than a century. They have always been ready to provide a transcript for every student, reporting all courses taken, grades received, honors awarded, and degrees conferred. Institutional research offices provide summaries of these student records to campus leaders, accreditation agencies, and the public. Why learning analytics now?
Two major trends drive the current emergence of learning analytics. First, data informing teaching and learning are increasingly extensive and accessible. Second, innovative new analytic approaches to digesting, visualizing, and acting on these data emerge every day. 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.
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.
This course is ideal for school teachers who want to improve their teaching through valuable data-driven insights. Do you want to be more reflective in your teaching practice and wonder if there are technologies that can help? Are you curious about how data-driven, evidence-based teaching practices can improve your students’ learning? This is the course for you!