NetGenEd Project

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.

Analytics marries large data sets, statistical techniques, and predictive modeling. It could be thought of as the practice of mining institutional data to produce "actionable intelligence."

Many institutions have implemented analytics to improve enrollment management. Institutional researchers collaborating with admissions staff have created complex formulas—based on standardized exam scores, high school coursework, and other information—to determine which applicants will be admitted

With the increased concern for accountability, academic analytics has the potential to create actionable intelligence to improve teaching, learning, and student success. Traditionally academic systems—such as course management systems, student response systems, and similar tools—have generated a wide array of data that may relate to student effort and success. Early academic analytics initiatives are seeking to predict which students are in academic difficulty, allowing faculty and advisors to customize learning paths or provide instruction tailored to specific learning needs.