Mining academic analytics to inform student success strategies
Chief Information Officer, SUNY Buffalo State
Learning analytics holds tremendous promise to help institutions of higher education improve student success. This is accomplished by mining repositories of academic activity to tailor the experience of each individual learner through customized learning activities, personalized support services and proactive intervention strategies. Data gleaned from institutional learning management systems can drive predictive analytics designed to identify activity patterns as they correlate to student success measures. This approach promises to assist educators and institutions to proactively respond to students in need and improve outcomes by intervening at the point of detection. The presentation will provide a broad overview of the current state of Learning Analytics and highlight current research in the field and their implications.