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Long-time e-Literate readers know that I have been a fan of the concept of learning analytics for a number of years now. But it became apparent at this year’s Learning Impact conference that learning analytics are the new hotness. Everybody is talking about them, and increasing numbers of vendors (LMS vendors, ERP vendors, textbook vendors, etc.) are trying to figure out how to get in on the party.

Just as businesses use social analytics to suggest personalized options to consumers based on their buying habits, individual teachers can use learning analytics to help them develop adaptive, personalized learning plans for individual students.

The School of One program, in use in several New York City public schools, uses learning analytics to develop personalized mathematics learning programs. The School of One’s learning algorithm conducts everyday assessments of a student’s learning style and math skills, and uses this to produce a personalized learning “playlist” for each student. This playlist is comprised of individual lessons in math, which are put into the order that the algorithm determines is optimal for the student’s math skills development. Certainly, School of One is quick to note that this is intended to supplement, not to replace, an individual teacher’s expertise. Similarly to how Hunch focuses on social media analytics as a consumer service, other learning analytics programs are being developed for use by both learners and teachers to enhance individual learning. Socrato is one such program that is primarily intended for students preparing to write standardized multiple-choice tests. It performs learner analytics on students’ online practice tests to identify which areas students most need to improve upon.

Evidence from top-performing schools shows that use of data, analysis, and feedback are our best tools for improvement.

The increasing use of online software and digital devices in classrooms presents an opportunity to collect high-frequency data for mining. Today’s analytics techniques could be used to develop a deeper understanding of how students learn, recommend personalized learning plans, and identify early warning flags. Rich data, analytics, and feedback enable a process of iteration and continuous improvement, where educators become learners, and we figure out how to improve education. We are at the beginning of a wave of data-driven change in education, with important social consequences and fantastic opportunities.