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Minnesota State Colleges and Universities Campus Locations

DEVELOPING A MODEL TO EXPLAIN IPEDS GRADUATION RATES AT MINNESOTA PUBLIC TWO-YEAR COLLEGES AND FOUR-YEAR UNIVERSITIES USING DATA MINING For more information contact: Brenda Bailey Ed.D. Associate Director for Research Minnesota State Colleges and Universities Brenda.bailey@so.mnscu.edu.

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Minnesota State Colleges and Universities Campus Locations

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  1. DEVELOPING A MODEL TO EXPLAIN IPEDS GRADUATION RATES AT MINNESOTA PUBLIC TWO-YEAR COLLEGES AND FOUR-YEAR UNIVERSITIES USING DATA MININGFor more information contact:Brenda Bailey Ed.D.Associate Director for ResearchMinnesota State Colleges and UniversitiesBrenda.bailey@so.mnscu.edu

  2. Minnesota State Colleges and Universities Campus Locations

  3. Background of the Problem • All postsecondary institutions are required to submit the IPEDS Graduation Rate Survey and disclose graduation rates for Student Right-to-Know • Reporting graduation rates without reporting supplementary information should be questioned (Astin, 1996) • Little is known about using IPEDS data to produce supplementary information about graduation rates at both 2-year and 4-year institutions

  4. Research Questions • What is the relationship between IPEDS graduation rates and institutional characteristics? 2. Given these relationships, what are the predicted graduation rates? 3. How do predicted graduation rates compare to actual graduation rates at Minnesota State system institutions?

  5. Significance • Done at institution level • Predicted graduation rates can provide context • Little prior research of 2-year college IPEDS data • No current research uses data mining on both 2-year and 4-year graduation rates • Identified new predictor variables

  6. “Data mining is the process of discovering hidden messages, patterns and knowledge within large amounts of data and making predictions for outcomes or behaviors” (Luan, p. 17).

  7. TRADITIONAL STATISTICAL APPROACH: Deductive Theory Hypothesis Observation Confirmation DATA MINING APPROACH: Inductive Theory Tentative Hypothesis Pattern Observation (Trochim, 2002)

  8. Fall Collection Winter Collection Spring Collection Employees by Assigned Position Survey Enrollment Survey Institutional Characteristics Survey Finance Survey Completions Survey Faculty Salaries Survey Student Financial Aid Survey IPEDS Peer Analysis System Fall Staff Survey Graduation Rates Survey Data Source: IPEDS Data Collection System

  9. IPEDS Peer Analysis System Step 1 Download IPEDS Data Microsoft Excel Files Step 2 Build Data Mining Files Microsoft Access and SPSS Software Step 3 Data Mining C&RT Clementine Software Weighted Predicted IPEDS Graduation Rates Microsoft Access Flow Chart of Data Analysis

  10. Algorithm Classification and Regression Tree (C&RT) • Tree-based classification and prediction method with binary splits • Examines input fields and splits records into peer groups with similar output field values • Graduation rate was set as the output variable • All other IPEDS variables were set as input fields • Variables can be nominal or ordinal (categorical) or interval (scale) • Predicted graduation rate is the average graduation rate for each peer group • The researcher also calculated a weighted predicted graduation rate for the institutions in each peer group.

  11. Strong Relationship Between Actual and Predicted Graduation Rate

  12. Source of Predictor Variables

  13. Public Four-year Model Predictors

  14. Public Two-year and Less Model Predictors

  15. Average male faculty salary Number of awards in Computer Science Number of service/maintenance men Regional accrediting agency No special learning opportunities offered CIP code of largest program Cost of books and supplies in largest program Calendar system Other expenses off campus GRS cohort as a percent of entering class Some New Predictors

  16. So What at Minnesota State System? • Could provide national context for Student-Right-to-Know Disclosure forms • Could provide national context for graduation rate reports and accountability measures • Identifies peers groups for Minnesota State system colleges and universities • Shows different predictors for different sectors and peer groups within the system • Data mining techniques could be used for other system research projects

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