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

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

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.

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?

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

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

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

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

Predictive Analytic Tools

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

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

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

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

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