Predictive Analytics

References

ECAR-Analytics Working Group. (2015). The predictive learning analytics revolution: Leveraging learning data for student success. ECAR working group.
Koedinger, K. R., McLaughlin, E. A., & Stamper, J. C. (2014). Data-driven Learner Modeling to Understand and Improve Online Learning: MOOCs and technology to advance learning and learning research (Ubiquity symposium). Ubiquity, 2014(May), 3.

Data Sets

LMS Data Sets

Activity from Event Logs

Achievement Data from Electronic Grade Books

High level general events, course site logins, individual content views on discussion forums, etc ✏

Grades associated with exams, assignments, and participation. 🖊

Predictive Modeling

focuses on student success, completion, and operations

used to forecast the number of courses needed to meet student demand, based on course taking behavior

by looking at what students click on instructors can analyze how a student will perform in the course

Interventions

Defining Processes

Setting Policies

Making Referrals for Other Academic Support Services

"Interventions should ideally have a specific, defined outcome and be measurable for effectiveness." (E-CAR)

Impact on Learners

Student Success Rates Increase

Graduation Rates Increase

Increased Course Completion Rates

References

ECAR-Analytics Working Group. (2015). The predictive learning analytics revolution: Leveraging learning data for student success. ECAR working group.
Koedinger, K. R., McLaughlin, E. A., & Stamper, J. C. (2014). Data-driven Learner Modeling to Understand and Improve Online Learning: MOOCs and technology to advance learning and learning research (Ubiquity symposium). Ubiquity, 2014(May), 3.