Learning analytics

Learning analytics is the measurement, collection, and analysis of data about students and their learning contexts, assignment scores, time spent on course materials, engagement in discussion forums, login patterns, and more, with the goal of understanding how learning is actually happening and where it is breaking down. It spans individual courses, whole institutions, and increasingly entire school systems.

Data typically comes from learning management systems, assessment platforms, and student information systems, and is used to build dashboards for instructors and advisors, or to power predictive analytics models that flag students at risk of falling behind or dropping out before final grades make it obvious. This is distinct from simple grade reporting, which looks backward at outcomes; learning analytics aims to surface patterns while there is still time to intervene.

Institutions use learning analytics to support data-driven decision-making about curriculum design, advising priorities, and resource allocation, and early-warning systems built on it are a common input into efforts to improve the student retention rate. Pitfalls include acting on correlations, such as low forum activity, that may reflect a student's genuine learning style rather than disengagement, and privacy and equity concerns around using sensitive student data to make consequential decisions without transparency to students and families about how the data is used.