Learning analytics refers to the collection, analysis, and reporting of data about learners and their contexts, with the goal of improving teaching, learning, and student outcomes. It enables institutions to make informed decisions through evidence derived from student behaviors, performance, and engagement patterns.
Learning analytics is used by faculty, instructional designers, and academic support teams to identify at-risk students, personalize learning pathways, and enhance curriculum effectiveness. It also supports institutional effectiveness, compliance, and strategic planning through actionable insights.
The process of analyzing student data to improve instructional strategies and learner success.
Data points that track student interaction with course content, peers, and instructors.
The use of historical and real-time data to forecast student performance and risk factors.
A visual interface displaying key indicators related to learning progress and outcomes.
Teaching strategies informed by real-time analytics and learner feedback.
A tool that flags students at risk based on academic performance and behavioral indicators.
Institutions are integrating learning analytics into LMS platforms, SIS systems, and mobile apps to provide real-time feedback loops. AI-powered analytics now support adaptive learning, automated interventions, and equity-focused tracking, reshaping how faculty and advisors engage with students.