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Everyone is talking about analytics applied to education and how “big data” can (will?) transform many aspects of our educational institutions. And for those of us passionate about improving opportunities for learning, the phrase “learning analytics” is particularly intriguing. So what do we mean by “learning analytics”? In short, it’s “the use of data and models to predict student progress and performance, and the ability to act on that information,” as defined in the Next Generation Learning Challenges (see the useful Educause Learning Initiative brief). Learning analytics overlaps with the somewhat broader phrase “academic analytics,” which encompasses other institutional bodies of data such as enrollments, graduation rates, and institutional outcomes tracking. A combination of learning analytics and academic analytics can provide an environment where administrators, advisors, faculty, and the students themselves have the data visualization tools they need for learner success. For example, what happens when students can see their own course participation & grade data and compare it to that of others (anonymously) in the same course, while the course is in progress, allowing them to change their behavior mid-course? This type of exposure to learning analytics can be a powerful motivator. Students become more aware of their activities and time on task in their courses. And while the technology is not comprehensive (some courses have more online activities than others), the balance between online and offline activities can be understood by the student despite the lack of data on the offline activities, once the student has crossed that important threshold of self-awareness. Based on this increased understanding of the value of specific behaviors, students can make better informed decisions about how to use their limited time and whether or not to change their behaviors. More detailed data can help students make more granular decisions, such as whether to spend more time reading and contributing to discussion forums or more time reviewing lectures to prepare for a test, based in part on comparisons to what other students are doing—which of course is varying in real time. This is learning analytics in action. Here is just a sampling of critical questions that can be addressed in part using learning analytics:

• How does student activity in Blackboard correlate to student success?
• How can we identify and promote effective teaching practices?
• How can we support student retention and degree completion?
• Can we predict when, where, and why learners hit challenges in their learning progress so that we can provide the right support at the right time?

There are extensive opportunities in this arena using solutions that are already available. As Mark Milliron has put it, let’s put our technology on purpose and apply learning analytics to helping students succeed in very real ways, today.