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Fostering Data Exploration to Achieve
Enrollment & Student Success Goals
JON MACMILLAN
Senior Data Analyst
JAMES COUSINS
Senior Statistical Analyst
Today
• What is Rapid Insight?
• Our work in higher education
• Enrollment and retention trends
• Product overview
• Q&A
EASY
AFFORDABLE
POWERFUL
ANALYTICS
Product overview
Veera Construct: Data prep and reporting
Veera Predict: Automated predictive modeling
Veera Bridge: Cloud-based sharing platform
Fostering Data Exploration to Achieve
Enrollment & Student Success Goals
JON MACMILLAN
Senior Data Analyst
JAMES COUSINS
Senior Statistical Analyst
Enrollment Trends
Source: National Student Clearinghouse
Enrollment Trends
Degree
Age
Intensity
Gender
First Time
Overall
Source: National Student Clearinghouse
Percentage Change in Spring 2018 Enrollment From Prior Year
Enrollment Trends
Source: National Student Clearinghouse
Enrollment Pipeline
Source: National Center for Education Statistics
Enrollment Pipeline
Source: National Center for Education Statistics
Persistence and Retention Trends
Source: National Student Clearinghouse
First-Year Students In All Sectors
Persistence and Retention Trends
First-Year Students By Race And Ethnicity, Fall 2016 Entering Cohort, Four-Year Public Institutions
Source: National Student Clearinghouse
Completion Trends
6-Year Completion Rates By Starting Term
Source: National Student Clearinghouse
Completion Trends
Six-Year Outcomes by Race and Ethnicity
Source: National Student Clearinghouse
What is Predictive Modeling
= Did not succeed
= Succeeded
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
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
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
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
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
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
SOFTWARE DEMONSTRATION
Thank you
For more information or to download a 14-day
trial, visit RapidInsight.com

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Fostering data exploration to achieve enrollment & student success goals

  • 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
  • 4.
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  • 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
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  • 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
  • 28. Thank you For more information or to download a 14-day trial, visit RapidInsight.com