1. Gabriela Garcia John Briggs

2. Explore whether using an assessment instrument which measures non-cognitive attributes is a predictor of student success as opposed to other variables.

3. Population Large, urban, public University Large Under Represented Minority (URM) (> 30%) ~40% need remediation in… English and/or Math

4. What are non-cognitive factors? Assessment of college readiness The student’s ability to navigate the demands of the college environment Ability to persist and graduate Sommerfeld, A. (2011) Recasting non-cognitive factors in college readiness as what they truly are: Non-academic factors. Journal of College Admissions, Fall, 18-22.

5. Student Strength Inventory (SSI) Campus Labs Instrument 48 scaled items 6 non-cognitive constructs Constructs Cronbach Alpha (.81 -.90) Predictive ability

6. Constructs Academic Self-Efficacy Academic Engagement Educational Commitment

7. Constructs Resiliency Campus Engagement Social Comfort

8. Constructs Retention Probability Academic Success

9. Administration Surveyed First-Time Freshman (FTF) Undergraduate Transfer (UGT) Beginning of 1 st Semester (Fall 2014) Solicited through the campus notification system Self-selected 24% participation rate FTF = 832 UGT = 852

10. Administration URM includes Native American, Black, and Hispanic Foreign includes students with residency outside the U.S. First generation includes students who are the first in their family to attend college.

11. Administration

12. At the end of the survey Score ranges low, moderate, or high Campus resources Class resources

13. Example of Recommendation: Educational Commitment High: Visit the Career Center (http://www.xxxx.edu/careercenter) to identify career options for your college degree.http://www.xxxx.edu/careercenter Talk to professors in your department or your academic advisor about undergraduate research or internship opportunities in your major area of interest.

14. Example of Recommendation: Educational Commitment Moderate Talk with your academic advisor or visit the Career Center (http://www.xxxx.edu/careercenter) to identify potential careers for individual with a college degree.http://www.xxxx.edu/careercenter Speak with your professors or individuals in your field(s) of interest about the value of a college education.

15. Example of Recommendation: Educational Commitment Low Talk with your academic advisor about the wide range of career options for an individual with a college degree or go to the Career Center’s website (http://www.xxxx.edu/careercenter) and explore different majors and careers.http://www.xxxx.edu/careercenter Speak with your professors or individuals in your field(s) of interest about the value of a college education.

16. Other Treatments None No follow-up No contact from staff, faculty or administrators

17. Measures of Student Success Persistence Failure Cumulative Units Cumulative GPA

18. Bivariate Correlation: Persistence vs. SSI Constructs First-Time Freshmen Undergraduate Transfers All Students

19. Bivariate Correlation: Failure vs. SSI Constructs First-Time Freshmen Undergraduate Transfers All Students

20. Bivariate Correlation: Cumulative Units vs. SSI Constructs First-Time Freshmen Undergraduate Transfers All Students

21. Bivariate Correlation: Cumulative GPA vs. SSI Constructs First-Time Freshmen Undergraduate Transfers All Students

22. Demographic/Academic Variables First-time FreshmenUndergraduate Transfer

23. Regression Standardize the independent variables Two step regression SSI Construct(s) SSI Construct(s) & Demographic/Academic Variables

24. Regression Dependent Variable: Measure of Student Success Model 1: Retention Probability (SSI) Model 2: Retention Probability (SSI) and Demographic/Academic Variables Model 3: SSI Constructs Model 4: SSI Constructs and Demographic/Academic Variables

25. Logistic Regression Persistence Failure

26. Logistic Regression

27. Linear Regression Cumulative Units Cumulative GPA

28. First-time Freshmen Models Persistence

29. Undergraduate Transfer Models Persistence

30. First-time Freshmen Models Failure in Class

31. Undergraduate Transfer Models Failure in Class

32. First-time Freshmen Models Cumulative Units

33. Undergraduate Transfer Models Cumulative Units

34. First-time Freshmen Models Cumulative GPA

35. Undergraduate Transfer Models Cumulative GPA

36. Other Groups Science, Technology, Engineering, & Mathematics (STEM) Freshmen Students Native Freshmen Students

37. Freshmen STEM Students Cumulative GPA

38. Freshmen STEM Students Cumulative Units

39. Freshmen Native Students Cumulative GPA

40. Freshmen Native Students Cumulative Units

41. Conclusion Non-cognitive provide little or no additional information in predicting student success for this institution Predictions can be made with academic/demographic variables

42. Caveats Treatment of participants. Recommendation(s) could have made the difference, especially for low performing students > 50% of the students tested were Undergraduate Transfers Large segment of URM students, the interpretation of questions might be subject to cultural perspectives

43. Caveats Large segment first generation students. Complete software package was not implemented. Advisors/Faculty did not reach out to students Campus services did not reach out to students SSI was used as a stand alone tool

44. Thank you to… Scott Heil Stuart Ho Chao Vang