1. Analytics for Student Success
October 24, 2018
Analytics for Student Success
Academic and Student Affairs Fall Leadership Conference

2. The Power of Data: How can data and analytics help you improve student success?
Lynn Akey
Minnesota State University, Mankato
Sue Carter
System Office Research
Eri Fujieda
Winona State University
Wendy Marson
Dakota County Technical College and
Inver Hills Community College

3. Academic and Student Affairs Strategic Vision
To be a national higher education leader in
transforming systems and practices to improve
student outcomes, eliminate educational disparities, and meet workforce needs.

4. Equity and Inclusion – The Lens or Framework for Initiatives
How is the initiative measuring success in terms of equity in outcomes?
What student outcomes (retention, completion, etc.) are being tracked for the initiative?
What equity and inclusion principles have been incorporated into the initiative?
In what ways have equity and inclusion competencies (cultural competence, culturally relevant curricula, equity-based policies, etc.) been part of the initiative?

5. System Office Projects: Updates
Predictive Analytics Pilot (Phase II)
Developmental Education Data Mart
Upcoming Data Releases

6. Predictive Analytics Pilot - Phase II
Predictive Analytics: Using historical data to determine patterns and develop models to predict future outcomes
Project Goals
More fully assess the benefits and value of predictive analytics for improving students' experience and supporting improved outcomes
Help formulate system approach to analytics & inform NextGen decisions
Give campuses an opportunity to try out predictive analytic software as part of their efforts to improve student success
Provide data for decision-making with a focus on equity and inclusion

7. Predictive Analytics Pilot - Phase II
Background/Phase I (ended in FY 2018)
1st Year: Develop data extracts (System) & begin intervention inventories (Campus)
2nd Year: Intervention inventories & begin work with analytic tools (Campus)
Challenges:
20 Institutions -- campus engagement with the tools varied greatly
Data work took significantly longer than expected
Efforts hindered in year two by significant changes in the tool/platform
Challenges left campuses with too little time/opportunity to really work with the data
Phase II?
Leverage work completed in Phase I with a smaller group of institutions
Focus on building a community of practice and assessing future needs

8. Predictive Analytics Pilot - Phase II
Two Year Contract with Hobson’s Starfish Analytics
2 Universities and 8 Colleges
Alexandria Technical & Community College
Anoka-Ramsey Community College
Anoka Technical College
Bemidji State University
Century College
Minnesota State University, Mankato
Northwest Technical College
Saint Paul College
South Central College
St. Cloud Technical & Community College

9. Predictive Analytic Tools

10. Predictive Analytics Pilot - Phase II
Where we’ve been (what has been accomplished to date)
Participating campuses identified during the summer & teams formed
October 18th kick-off meeting-Hobsons, System IT and campus teams
IT is moving forward on infrastructure/data architecture needs
Phase II contract is patterned on those for other Starfish products and allows for student identifiers to produce student watch lists and to student level data in other Starfish products)
Where we’re going (primary focus for coming two months)
Complete IT infrastructure and data work at system office
Campuses work directory with Hobsons on understanding the data & training on the tools
Campuses start to use the analytic tools
Quarterly meetings/sharing of work being done

11. Developmental Education Data Mart
First released Fall 2017
Comprehensive databases about developmental education students, courses and outcomes
Designed to support campus research, required legislative reporting and continuing work on implementing the Developmental Education Strategic Roadmap
Longitudinal analysis of undergraduate degree and certificate seeking students from entry through graduation
Two levels of data:
Student level data files support research
Analysis Tool supports exploration of measures and disaggregation of data to identify gaps in outcomes

12. Developmental Education Data Mart & Reporting
Measures:
Students taking developmental courses
Completion of developmental courses within one or two years
Completion of college level Math and English courses within one or two years
Completion of 20 or 30 credits over one or two years
GPAs and cumulative credits
Persistence and completion rates
Graduation/completion rates
Credits earned at graduation
Data for both those taking developmental courses and those not taking developmental course work
Disaggregated by developmental course subject area, and student characteristics such as race-ethnicity, Pell eligibility, gender, age, first generation status, student credit load and admission status

13. The Developmental Education Data Mart & Analysis Tool are Available through College and University Institutional Research

14. Data Marts and Analysis Tools
Existing Tools and Data Sets
Enrollment Data and Tools (Public and Internal)
Persistence and Completion Data and Tools (Public and Internal)
Transfer Tables incorporating National Student Clearinghouse Data (Internal)
Upcoming Data Releases (Internal)
Graduate and Graduate Follow-up Data
Transfer Credits
The tools and data sets allow for disaggregation by a wide variety of student characteristics (e.g., race-ethnicity, Pell eligibility, gender, age, first generation status, student credit load and admission status) to allow for examining gaps in students outcomes