Mining the LMS: Identifying Transactional Elements for Prescriptive Analytics
Thursday, October 27, 2016 | 12:30PM–1:30PM | Exhibit Hall B/C, Level One
Session Type:
Poster Session
Delivery Format:
Poster Session
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
