1. Sr. Vice President, Student Success
Utilizing Predictive Analytics to Identify Trends and Patterns that Support Student’s Overall Success
Diane Recinos, Ed.D.
Sr. Vice President, Student Success

2. Berkeley College 8 Locations New York, New Jersey & Online
Diverse student body
Demographic
Onsite
Online
Female
66%
79%
Male
34%
21%
22 or older
67%
92%
Full-time
52%
Part-time
33%
48%
Certificates, Associates, Bachelors & MBA

3. Challenges Our Students Face
Working FT and going to college
Course Load
Work life balance
Financial commitments
Family obligations
Technology limitations

4. Predictive Analytics Predicted Persistence for all students
The capability to identify students that require additional support and provide them with the resources/confidence they need to be successful when they needed most.
Predicted Persistence for all students
Early intervention and ability to react
Assessment of interventions
Effectiveness of courses offered

5. Retention and Graduation
Higher Ed Focus
Retention and Graduation
Ability to…
identify students at risk early
provide support when students need it most
motivate and encourage students
identify other factors that could impede success

6. Proactive vs Reactive Implemented Predictive Analytics
Campus wide initiative
Utilize data to develop a strategic approach or new initiative
Ability to focus on various cohorts of students
First time full time
Students not making Satisfactory Academic Progress (SAP)
International
Veterans
Credits Earned (freshman, sophomore, junior, senior)

7. Predicted Persistence
Thousands of data points
Data model utilizing historical data
Various filters
Prediction distribution
Powerful predictors

11. Trends and Patterns Students who persist at a higher rate:
Enrolled in online and onsite courses
Register earlier for the next semester
Have a full time course load
22 years of age and older
Pay more out of pocket cost

12. Trends and Patterns Cont.
Students who persist at a higher rate:
Enrolled in a Bachelor’s Degree
Credits earned vs attempted
Cumulative GPA
Have transfer credits

13. Impact of Predictive Analytics on Academic Advising
Intentionally moved to a cohort model
Decrease advisor to student ratio
Average cohort size of
Meaningful advising sessions
Relationship building
Increase student touchpoints

14. Early Intervention & Ability to React
Tool designed specifically for Academic Advisement
Cohort advising
Clear focus of what was happening with their students
Universal approach to working with their cohort
Data informed opportunities for student discussions
Caseload management and filtering functionality
CRM
All student interactions are documented (phone calls, s, text messages)
360° view of student

15. Features Probability buckets Advanced filters Persistence probability
Probability shift
GPA
Academic Advisor
Sample data provided by Civitas

16. Analysis and Assessments

17. Course Analytics
How much does a student’s grade in a particular course influence graduation likelihood?
What course grades predict student success?
What is a course’s potential to boost graduation rates?

19. Avoid Big Data Pitfalls
Start with clean data
Don’t make assumptions
Know the question you want answered
Segment student populations
Have qualified individuals analyze the information
Don’t assume everyone is enthusiastic

20. Additional Tools to Promote Student Success
“Action is the fundamental key to success.” – Pablo Picasso

21. Degree Map – Planning Success
User friendly and visually appealing
Students can track their progress
Review remaining requirements
Tool for Academic Advisors while meeting with students
“What if” scenarios for degree changes
Explore careers

22. STUDENT-ENABLED PLANNING
Sample data provided by Civitas

23. My Scheduler – Scheduling Success
Expand self-service options
Empower students
Reduce registration frustration
Mobile friendly
Build schedules that accommodate outside obligations

24. FOUR EASY STEPS TO OPTIMAL SCHEDULES
1. ADD COURSES
2. ADD BREAKS
3. VIEW SCHEDULE
OPTIONS
REGISTER
Sample data provided by Civitas

25. “You can have data without information, but you cannot have information without data.”
Daniel Keys Moran, an American computer programmer and science fiction writer.