Mining the LMS: Identifying Transactional Elements for Prescriptive Analytics

SUNY Buffalo State has adopted a learning analytics approach that mines transactional student activity data within the LMS to identify risk behaviors. Combining risk data with the assessment of intervention approaches has facilitated a prescriptive analytics framework designed to foster student success through automated, intrusive advising strategies.

Outcomes: Understand the utility of prescriptive analytics *Learn how to use decision tree logic for prescriptive interventions *Learn about transactional risk factors accessible in the LMS database