This document summarizes a presentation about using data analytics to improve student enrollment and retention. It discusses trends in enrollment, persistence, and completion rates based on data from national sources. It also provides an overview of Rapid Insight's analytics software for data preparation, predictive modeling, and sharing results. Additionally, the presentation discusses using predictive modeling to identify metrics that affect student success and retention at different points. Examples of factors that may be considered in the models are listed. The last sections discuss institutions' use of analytics for student success based on a landscape analysis and the importance of transparency when using predictive modeling.

1. Fostering Data Exploration to Achieve
Enrollment & Student Success Goals
JON MACMILLAN
Senior Data Analyst
JAMES COUSINS
Senior Statistical Analyst

2. Today
• What is Rapid Insight?
• Our work in higher education
• Enrollment and retention trends
• Product overview
• Q&A

3. EASY
AFFORDABLE
POWERFUL
ANALYTICS

7. Product overview
Veera Construct: Data prep and reporting
Veera Predict: Automated predictive modeling
Veera Bridge: Cloud-based sharing platform

8. Fostering Data Exploration to Achieve
Enrollment & Student Success Goals
JON MACMILLAN
Senior Data Analyst
JAMES COUSINS
Senior Statistical Analyst

9. Enrollment Trends
Source: National Student Clearinghouse

10. Enrollment Trends
Degree
Age
Intensity
Gender
First Time
Overall
Source: National Student Clearinghouse
Percentage Change in Spring 2018 Enrollment From Prior Year

11. Enrollment Trends
Source: National Student Clearinghouse

12. Enrollment Pipeline
Source: National Center for Education Statistics

13. Enrollment Pipeline
Source: National Center for Education Statistics

14. Persistence and Retention Trends
Source: National Student Clearinghouse
First-Year Students In All Sectors

15. Persistence and Retention Trends
First-Year Students By Race And Ethnicity, Fall 2016 Entering Cohort, Four-Year Public Institutions
Source: National Student Clearinghouse

16. Completion Trends
6-Year Completion Rates By Starting Term
Source: National Student Clearinghouse

17. Completion Trends
Six-Year Outcomes by Race and Ethnicity
Source: National Student Clearinghouse

18. What is Predictive Modeling
= Did not succeed
= Succeeded

21. Student Success
Retention/Attrition Modeling
What metrics matter at the start of first year?
• Address
• Demographic
• Test scores
• HS GPA
• Financial aid
• Application date
• Campus visits
• Orientation attendance
• Housing
• Enrollment intensity
• DFW course enrollments

22. Student Success
Retention/Attrition Modeling
What metrics matter at the start of 2nd Semester?
• Address
• Demographic
• Test scores
• HS GPA
• Financial aid
• Application date
• Campus visits
• Orientation attendance
• Housing
• Enrollment intensity
• DFW course enrollments
• End of term GPA
• LMS interactions
• Advisor contacts
• Academic standing
• Survey data
• Campus engagement

23. Student Success
Retention/Attrition Modeling
What metrics matter beyond 2nd Semester?
• Address
• Demographic
• Test scores
• HS GPA
• Financial aid
• Application date
• Campus visits
• Orientation attendance
• Housing
• Enrollment intensity
• DFW course enrollments
• End of term GPA
• LMS interactions
• Advisor contacts
• Academic standing
• Survey data
• Campus engagement
• Cumulative GPA
• GPA trend
• % Courses D/W
• Student mentor
• Scheduling tendencies

24. Institutions’ Use of Data and Analytics for
Student Success
Results From a Landscape Analysis
“As higher education institutions in
the United States strive to maximize
their use of resources to better
support students, it is now even more
critical for professionals to make
data-informed decisions.”data-informed decisions

25. Institutions’ Use of Data and Analytics for
Student Success
Results From a Landscape Analysis
94%
88%
79%
54%
40%
24%
12%
5%
8%
13%
28%
39%
12%
20%
1%
4%
7%
18%
21%
64%
68%
IN CONDUCTING STUDENT SUCCESS STUDIES, PRIVACY RIGHTS ARE RESPECTED.
TO STAY COMPETITIVE, WE MUST CONTINUE TO INVEST IN STUDENT SUCCESS ANALYTICS
THE DATA USED FOR STUDENT SUCCESS ANALYTICS ARE ACCURATE
THE RESULTS OF ANALYTICS STUDIES ARE USED PROPERLY; WRONG CONCLUSIONS ARE NOT
DRAWN
WE ARE ABLE TO IMPLEMENT THE RESULTS OF STUDENT SUCCESS ANALYTIC STUDIES EFFECTIVELY
I AM CONCERNED THAT MY INSTITUTION DEPENDS ON THE QUALITY OF VENDOR ALGORITHMS THAT
WE DO NOT FULLY UNDERSTAND
I AM CONCERNED THAT MY INSTITUTION RELIES ON BLACKBOX ALGORITHMS TO INFORM DECISIONS
Strongly Agree or Agree Neutral Strongly Disagree or Disagree

26. Being Transparent
• Find a champion for the predictive modeling effort
• Communicate regularly
• Make the data available
• Train staff on how to interpret and use the data
• Maintain the importance of stakeholders
• Validate your model

27. SOFTWARE DEMONSTRATION

28. Thank you
For more information or to download a 14-day
trial, visit RapidInsight.com