Modeling Student Success Using National Survey of Student Engagement Data

2. Modeling Student Success Using National Survey of Student Engagement Data Mardell A. Wilson & Renée M. Tobin Illinois State University

3. Illinois State University: Who We Are • DRU Fall 2009 census day figures • Total enrollment: 20,856 • Undergraduate enrollment: 18,344 • 57.0% of all students are female • 12.0% of all students are minority

4. Illinois State University: Who We Are • 96.8% of undergrads are from Illinois • 93.1% of undergrads are < 24 yrs old • 94.4% of undergrads are full-time students

5. Illinois State University: Who We Are Fact Book (Planning and Institutional Research) – Fall 2009 • Who are they as high school students? ACT Mean Composite ScoresIllinois State 24.3State of Illinois 20.8National 21.1

6. Illinois State University: Who We Are Transfer Students • 1,926 new transfer students in fall 2009 • ~50% of bacc degrees granted annually • 83.3% retention from first-year to sophomore

7. University Assessment OfficeIllinois State University • UAO Office of the Provost • IR Finance and Planning The two offices work closely; however, the UAO is responsible for the management of all program level assessment.

8. Surveys of Student Engagement • NSSE [2000 pilot, 2001-2005, 2007, 2010 • FSSE [2006, 2011] • BCSSE [2006, 2009] Increased accountability of our resources has made it essential that we more carefully examine how surveys, such as those in the NSSE family, guide the campus beyond providing basic talking points.

9. Purposes of the Study • Examine NSSE data • Cross-sectional • Longitudinal • Are NSSE benchmark scores internally consistent in cross-sectional and longitudinal data models; and • Do NSSE benchmark scores predict a college student’s final GPA?

10. Method

11. Participants

12. Race/Ethnicity

13. Additional SampleDemographic Information • Gender • 1396 males (30.8%) • 3137 females (69.2%) • Mean ACT Score = 22.87 (SD = 3.58) • Mean High School GPA = 3.44 (SD = 0.37)

14. NSSE Benchmarks • Student/Faculty Interactions • Supportive Campus Environment • Level of Academic Challenge • Active and Collaborative Learning

15. Results

16. Preliminary Findings • Sample represents population • ACT scores • High school GPA • Positive change over time

17. Reliability of NSSE Benchmarks(Cronbach’s α)

18. Predicting Cumulative GPA with Cross-Sectional Data • Hierarchical Linear Regression • (Aiken & West, 1991) • Step 1: Control variables (e.g., high school GPA, ACT score) • Step 2: Benchmark scores and class rank • Step 3: Benchmark x Class rank cross products

19. LAC Benchmark and Class Rank as Predictors of Cumulative GPA

20. ACL Benchmark and Class Rank as Predictors of Cumulative GPA

21. Cross-Sectional Findings • High school GPA and ACT • Class rank • NSSE Benchmark scores

22. Results: Longitudinal ANALYSIS

23. Predicting Cumulative GPA with Longitudinal Data • Total of 127 students • Examined change in NSSE benchmark scores • High school GPA and ACT scores • NSSE Benchmark scores • Greater total variance

24. Discussion

25. Value of Data • Both approaches offer modest support • Cross-sectional vs. longitudinal approaches • Large sample sizes Power to detect effects • LongitudinalAccounts for more variance in GPA • Potential for future

26. Implications • Helps with accountability • More variance accounted for in longitudinal approach • Change over time suggests value added by institution

27. For more details… • Fuller, M. B., Wilson, M. A., &Tobin, R. M. (in press). The National Survey of Student Engagement as a predictor of undergraduate GPA: A Cross-Sectional and Longitudinal Examination. To appear in Assessment & Evaluation in Higher Education. • www.assessment.ilstu.edu

28. Questions?

29. Renée M. Tobin, Ph.D. Acting Director, University Assessment rmtobin@ilstu.edu Mardell A. Wilson, Ed.D. AVP for Academic Fiscal Management mawilso@ilstu.edu 309.438.2135 www.assessment.ilstu.edu