1. Retention Conference
Going the Extra Mile: Data-Driven, Student-Focused Retention Strategies That Work
Uday Sukhatme - June 16, 2017
Data
Predictive analytics – identify at-risk students
Targeted interventions - assessment

2. Data – past and present
Analysis of past data and behavior to predict future outcomes and improve them
Predictive analytics : One output variable y , for example, attrition probability Many input variables x1, x2, x3, … , for example, HSGPA, SAT, Pace GPA, early alerts, EFC, class attendance, Pace aid, federal aid, honors, veteran, international, events, legacy, homework, social events, campus-home distance,…)
Need complete, reliable data
as a function of time
Crystal balls … predict the future !!!

3. Predictive analytics - identify at-risk students
APPLICATIONS AND STRATEGIES 1
Academic, financial,
social/other
input variables +
Output attrition flag
(from previous years)
Regression
analysis determines constants Ci – model is now fixed
Use model to compute attrition probabilities – “scoring”
Regression analysis contains a linear combination of input variables C0 + C1*x1 + C2*x2 + C3*x3 + ….. to make a “best fit” to the output variable y
Constants Ci give relative importance of various input variables
Scoring breakdown by student helps identify the areas where students need help
Targeted interventions optimize limited resources

4. Targeted interventions - assessment
Some Pace activities:
Timely advising - advisor training, development, support
Pace Path – year plans, alumni mentoring, RISE activities
Pre-matriculation - developmental math, cultural immersion, financial counselors
Improved orientation course, embedded tutoring, learning communities, late start courses, technology, Starfish, first year interest groups, Women’s Empowerment Network, …
Pace initiatives are described in the Retention Initiatives Progress Report, compiled by the Division of Student Success –
Identify, engage, challenge,
plan, help, assess
Student success