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University of Michigan

Practical Learning Analytics

University of Michigan via Coursera

This course may be unavailable.


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.[1] 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.

What’s special about this course?

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.

Who should take this course?

Practical Learning Analytics should provide something for anyone interested in higher education: current, former, or future students, policy makers, academic advisors, data scientists, university administrators, ed-tech entrepreneurs, faculty members, even the curious public.

This course has been designed to work for a wide variety of audiences. Its structure is modeled on something everyone can enjoy: a smörgåsbord – we’re going to treat the class like one big meal. After we set the table, each guest may wander the room, taking either a small plate or a full entrée from a series of courses we offer up. When everyone is full, we’ll gather again over coffee to hear what people thought. There is no defined way to pursue such a meal – each diner chooses what’s right for them. And there is no defined way to take this course – every student must choose what’s right for them.

The course will open with a two week introduction, exploring the landscape of learning analytics in higher education and setting the table for the main event. This is followed by a four week meal during which participants may choose among an array of five different topics, each presented at two levels: a small plate providing a quick introduction, or a more filling entrée. Those choosing small plates will still have the opportunity to work with realistic data, analyzing it with code we provide. Those choosing entrées will make creative contributions of their own: writing new code for analysis or visualization of the data we provide, perhaps bringing in data of their own. After this month of exploration, the final two weeks will feature a concluding coffee. In them, we’ll review what students learned while wandering through all five courses, share the best things class members invented, and provide some concluding remarks.

[1] Try searching for “learning analytics” in the Google Ngram server…nothing comes up!


The menu:

Our smörgåsbord will include five major courses, each offered in both small plate and full entrée sizes. Each course will provide both a realistic data set and a set of example R code which can be used to conduct the basic analyses we will discuss. Small plate users will watch a few video lectures about their topic, complete a short quiz on the content, download the data and R code, and run an analysis to answer some simple questions. Users who choose the entrée will go further, extending the code in both instructor-specified and student defined ways. The really ambitious will repeat and extend these analyses using their own, local data. An introductory video for each course will outline what it includes and provide some sense of what users at each level will experience.

To keep the focus on the practical, the five courses are designed to explore analyses of interest to different audiences: students, instructors, department leaders, campus-wide leaders, and course designers.

  1. LA for students: How to become the student you want to be? Exploring courses, majors, comparing your performance to others realistically and richly.
  2. LA for instructors: Performance prediction in a course: up to and including grade penalties, placement analyses, performance disparities and their correlates, course-to-course correlation
  3. LA for department leaders: Persistence in a major, first through short course sequences and then from intention to degree
  4. LA for college/university leaders: Characterizing the student experience, program evaluation – observing differences and probing impact, capturing more and better information, comparing the experience of different groups.
  5. LA for course designers: What affects performance – behavior measurement, establishing the evidence basis for advice, then acting to affect performance with technological and human behavior change techniques, putting real-time data to work – early warning systems and personalized communication


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