College Graduates- How Can Hindsight become Foresight?

1. College Graduates- How Can Hindsight become Foresight? Gary Greer Assistant Dean, University College University of Houston Downtown & Chantel Reynolds Higher Education Assessment Manager The College Board September 9, 2010 NCTA Atlanta

2. Aim of this presentation Let’s celebrate data that can focus bright light on prediction of student success. Let’s simulate a world where we could get the data we want. Let’s define student success. Let’s create models that predict student success. Let’s simulate assembling of evidence. Let’s prepare our appeal for increased access to specific (predictive) data.

3. Rationale: let’s assemble Graduates’ pathways to success and Correlates of graduates’ success. Definitions of success (graduation, BBet, CBet, re-enrollments from term to term, year to year.) Inventories of successful students’ characteristics (scores, statuses.) (*consider that Abraham Maslow studied successful, happy humans).

4. Measurement • Independent variables can predict (IVs) • Placement scores, student ratings, faculty ratings predict • Dependent variables can be predicted (DVs) • GPAs, grades, re-enrollments, graduations can be predicted

5. Every university should have its list of Researchable Questions • To what extent is graduation predicted by demographic variables (age, gender, ethnicity, low income status)? • To what extent is graduation predicted by cognitive variables (placement scores, earning B or better in key courses, re-enrolling across terms)? • To what extent is B or Better predicted by placement scores?

6. How can we predict success? • Obtain IVs: placement scores and ratings. • Obtain DVs: grades, re-enrollment data, GPAs, graduations. • Predict likelihood of making a B or better, of re-enrolling, of graduating. • Construct a model. • Interpret findings. • Generalize to future students.

7. Simulated data for 5 students

9. Logistic Regression Statistics • Permit odds ratio conclusions. • Permit likelihood conclusions. • Are easily interpreted, that is, these statistics can be explained with words.

10. Predictions can be made Logistic Regression [output] enables us to make statements about likelihood: For example, “a low income student is .11 as likely to graduate (compared to non- low income).” For example, “a student who makes B or Better in College Algebra is 1.99 times as likely to graduate. For example, a student who re-enrolls for 6 semesters is 2.54 times more likely to graduate.”

11. Conclusions, what if . . . • What if we had our Research questions ready. • And what if we had the data. • And what if we could predict student success. • And what if our cultures of evidence were well designed. • And what if our students could trust that by way of good data we understand

12. Limitations of these conclusions • Researchers not asking important questions. • Researchers not collecting researchable variables of success. • Unreliability of data (grades). • Intervening variables (instructor impact). • Personal variables (motivation, respect, interest). • Restriction of Range (scores below the cut are not used since those scores do not get into the course).

13. To get College Board help Google “aces placement validity”

14. Thank You Let’s talk further. greer@uh.edu713 221 8101 office713 471 9178 cell