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

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

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

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

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

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)

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

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

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

Impact of Predictive Analytics on Academic Advising Intentionally moved to a cohort model Decrease advisor to student ratio Average cohort size of 180-200 Meaningful advising sessions Relationship building Increase student touchpoints

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, emails, text messages) 360° view of student

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

Analysis and Assessments

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?

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

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

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

STUDENT-ENABLED PLANNING Sample data provided by Civitas

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

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

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