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Statewide Unit Record Databases in Higher Education: Growth and Application

Statewide Unit Record Databases in Higher Education: Growth and Application. Peter Ewell National Center for Higher Education Management Systems (NCHEMS) PESC Annual Meeting April 29, 2008. State-Level “Unit Record” Databases in Higher Education.

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Statewide Unit Record Databases in Higher Education: Growth and Application

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  1. Statewide Unit Record Databases in Higher Education: Growth and Application Peter Ewell National Center for Higher Education Management Systems (NCHEMS) PESC Annual Meeting April 29, 2008

  2. State-Level “Unit Record” Databases in Higher Education • Established and Maintained by Public University System and SHEEO Offices • Several Decades of Experience at this Point • Originally Designed to Drive State Funding Formulas for Public University Systems • Used More Recently to Calculate Student Retention and Graduation Rates for Accountability Purposes (“Student Right to Know”), and to Track Students from One Institution to Another (“Enrollment Swirl”) • Federal Unit Record Proposal for Postsecondary Education

  3. State Unit-Record Database Inventory • Updated a Previous Inventory Conducted in 2003 • Looked at 49 Databases in 42 States • Contents Cover 81% of Nation’s Headcount Enrollment • Growing Number of Independent Colleges are Included • Reasonably Compatible Data Structures and Definitions for Core Data Elements (Largely Based on IPEDS and the “Common Core of Data”)

  4. Some Common Features Across States • Multiple Databases in Some States • Growing Experience with Linking Data to Other State Databases (K-12, UI-Wage, DMV, etc.), but this is Still a “Frontier” to be Explored • Virtually All Still Use SSN in Some Form as Key Link • FERPA and Privacy Issues are Major and Growing Concerns • Many Systems Getting Old and Hard to Maintain, and State Money to Do This is in Short Supply

  5. Some Specific Features • 23 SURs Contain Transcript-Level Detail • 17 SURs Have Data on Placement Test Scores and Participation in Developmental Education • 25 SURs Have Contain Financial Aid Records • 23 SURs Now Have Mode of Delivery Indicators (e.g. Distance Delivery, etc.)

  6. Commonly-Reported Challenges • Data Quality and Data Audit Functions • Lack of Analytical Capacity and Analytical Staff • Non-Credit Activities • Non-Traditional Calendars and Teaching/Learning Environments • Political and Organizational Issues

  7. Typical Reports Generated Through SURs • Basic IPEDS Reporting • Multi-Institutional Retention and Graduation Reports (In-State Only) • Reports on the Effectiveness of Developmental Education • High School Feedback Reports • Reports on Workforce Placement, Earnings, and Return on Investment

  8. SUR System Components Needed for Effective Longitudinal Tracking • Broad Coverage of the State’s Postsecondary Institutions (2-Year, 4-Year Public, Independent) • Agreed-Upon “Key Links” for Merging Term Records to Create Longitudinal Data Files (and the People to Do This) • The Data Elements Needed to Construct Key Performance and Outcome Measures • Paths to Link Longitudinal Data with External Databases (e.g. High School, Employment)

  9. Data Element Contents Needed for Effective Longitudinal Tracking • Basic Enrollment and Completion Data (Credits Attempted and Earned, GPA, Program Enrollment, Developmental Enrollment, Degrees Awarded) • Requires Census Date and End of Term Extracts • Demographic Data of Interest for Disaggregation (Gender, Race/Ethnicity, Age, Location [Income]) • Transcript-Level (Class-Level) Data is the “Gold Standard” for Effective Tracking

  10. Some State Examples of Using SUR Data • Florida K-20 Data Warehouse and Associated FLCCS Studies on High School and College Performance • Washington SBCTC Studies on Pathways to Success for Low Skilled Adult Students • Validating Placement Testing Policies for North Carolina Community Colleges • Data Sharing Among High Schools, Community Colleges, and Four-Year Colleges (CalPASS)

  11. Some Lessons from Experience • Data Systems Can Acquire a “Logic of their Own” • Data Use Drives Data Quality • Just “Having Good Data” Doesn’t Guarantee Good Policy • Secondary and Postsecondary SUR Development Still on Parallel Independent Tracks

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