1. The Relationship between the Admission Index and the Student Success at the University of Puerto Rico at Mayagüez Dr. David González Barreto Dr. Antonio A. González Quevedo Office of Institutional Research and Planning University of Puerto Rico at Mayagüez Presented at the AIR Forum 2005 San Diego, California June 1, 2005

2. Background Information University of Puerto Rico at Mayagüez (UPRM) is part of the University of Puerto Rico (UPR) system that consists of 11 Colleges and Universities. UPRM consists of four colleges and has a student population of more than 12,000 student out of which 1,000 are graduate students. UPRM is the only campus of UPR which has a College of Engineering and a College of Agricultural Sciences. UPR system has a Board of Trustees responsible for establishing institutional policies for all the units.

3. Objectives Show the profile of incoming freshmen from 1990-2003 at the University of Puerto Rico at Mayagüez: »Admission index (AI) »Type of high school »Gender »High school grade point average (GPA) »College Board Scores in Aptitude and Achievement Tests Using the University of Puerto Rico Admission Policy, present recommendations for UPRM regarding a model that will associate entrance profile with student success as defined by their for the first year GPA by colleges.

4. Outline of the Presentation Incoming freshmen profile Prediction models Comparison of models Conclusions and recommendations Future studies

5. Database The profile is based on data for 30,218 incoming freshmen to UPRM from 1990 – 2003. Models to evaluate if the system admission policy is suitable for predicting success at UPRM uses a subset of data from 1995-2003. Data Cleaning - 1.76% of the incoming freshmen were not included due to errors in the data or lack of completeness.

6. Profile of incoming Freshmen

7. Incoming Freshmen by Type of School

8. Incoming Freshmen by Gender

9. Admission index (AI) Index calculated for each prospective freshmen and used by the University of Puerto Rico system to decide who are admitted. The admission index formula was changed by the Board of Trustees for the incoming class of 1995 The index includes three components: the high school grade point average, College Entrance Examination Board (CEEB) score for Verbal Aptitude (Spanish), CEEB score for Mathematical Aptitude The high school GPA has a weight of 50% of the value of the admission index, while the Mathematical and Verbal Aptitude each represent 25% of the AI.

10. Average Admission Index by Year

11. Average Admission Index by Type of School

12. Average Admission Index per Year - Arts

13. Average Admission Index per Year - Sciences

14. Average Admission Index per Year – Agricultural Sciences

15. Average Admission Index per Year – Business Administration

16. Average Admission Index per Year – Engineering

17. Average GPA per Type of School

18. HS and 1 st Year GPAs per Type of School

19. Math Aptitude per Type of School

20. Average Verbal Aptitude per Type of School

21. Average Spanish Achievement per Type of School

22. Average Mathematical Achievement per Type of School

23. Average English Achievement per Type of School

24. Summary of Incoming Students Profile Average high school grade point average is higher for public schools students when compared to private schools students. Average first year grade point average is higher for students coming from private schools. Average CEEB scores have decreased for the duration of this study with the exception of the English Achievement component. Average CEEB scores were higher for all six components for private school students.

25. Comparison with USA Trends 1 Shift in responsibilities for developing admission criteria and standards from the admissions office to the state or governing bodies. Four year institutions continue to raise their academic qualifications for new students. Over 90% of institutions continue to require admissions test scores. High school GPA or rank is the most important factor in admission. Achievement test scores were not view as highly important. 1 Taken from, Trends in College Admission 2000, by Hunter Breland, James Maxey, Renee Gernand, Tammie Cumming and Catherine Trapani. Can be downloaded from the AIR site.

26. Comparison with USA Trends (continued) In addition to GPA, other admission factors in order of importance are: –ACT or SAT I scores –Pattern of high school coursework –Letters of recommendations and essays (more prevalent in private institutions –Interviews California has recently proposed that aptitude test scores be replaced by achievement test scores

27. Comparison with USA Trends (continued) Trends over time in importance of admission factors –The percentage of institutions for which high school GPA or rank is “very important” has increased steadily since 1979 –The percentage of institutions for which high school GPA or rank is the single most important factor has decreased steadily –Admission test scores show a steady increase as a “very important” factor has increased steadily

28. Prediction Models Models were based on predicting the first year grade point average based on the high school great point average, and the five CEEB scores Model: 1 st Year GPA = f(GPA, Verbal Aptitude, Mathematical Aptitude, English Achievement, Mathematical Achievement, Spanish Achievement) + ε

29. Prediction Models

31. Best Subsets Methods – All Colleges Vars R-Sq(adj) Mallows C-p GPA GPA APT_VERBAPT_VERB APT_MATEAPT_MATE ACH_ING ACH_ING ACH_MAT ACH_MAT ACH_ESP ACH_ESP 119.34263.5X 225.71515.6X X 328.1498.7X XX 429.178.2X XXX 529.39.4XX XXX 629.37XXXXXX Actual26.21300.4XXX

32. Best Subsets Methods – College of Engineering Vars R-Sq(adj) Mallows C-p GPA GPA APT_VERBAPT_VERB APT_MATEAPT_MATE ACH_ING ACH_ING ACH_MAT ACH_MAT ACH_ESP ACH_ESP 111.51743.4X 219.5618.1X X 321.6324.2X XX 422.8165.9X XXX 523.737.5X XXXX 623.97XXXXXX Actual20.8438.0XXX

33. Best Subsets Methods – Arts and Sciences : Sciences Vars R-Sq(adj) Mallows C-p GPA GPA APT_VERBAPT_VERB APT_MATEAPT_MATE ACH_ING ACH_ING ACH_MAT ACH_MAT ACH_ESP ACH_ESP 114.81767.1X 224.2667.3X X 328228X XX 429.473.8X XXX 529.822.4XX XXX 6307XXXXXX Actual25.4529.8XXX

34. Best Subsets Methods – Arts and Sciences : Arts Vars R-Sq(adj) Mallows C-p GPA GPA APT_VERB APT_VERB APT_MATE APT_MATE ACH_ING ACH_ING ACH_MAT ACH_MAT ACH_ESP ACH_ESP 115.5458.9X 220.4187XX 322.472.2XX X 423.419.1XX XX 523.75.3XX XXX 623.67XXXXXX Actual21.4129.4XXX

35. Best Subsets Methods – College of Agricultural Sciences Vars R-Sq(adj) Mallows C-p GPA GPA APT_VERBAPT_VERB APT_MATEAPT_MATE ACH_ING ACH_ING ACH_MAT ACH_MAT ACH_ESP ACH_ESP 112.3231.3X 216.6104.6X X 3/Actual18.840.1XXX 419.714.4XXXX 5206.9XXXXX 6207XXXXXX

36. Best Subsets Methods – College of Business Administration Vars R-Sq(adj) Mallows C-p GPA GPA APT_VERBAPT_VERB APT_MATEAPT_MATE ACH_ING ACH_ING ACH_MAT ACH_MAT ACH_ESP ACH_ESP 112.7557.6X 218.5205.9X X 320.493.1X XX 421.524.7XX XX 521.86XX XXX 6 7XXXXXX Actual18.9183.4XXX

37. Summary of comparison of models The model with three variables that best predicts 1 st year GPA contains the following variables: High school GPA, Mathematical Achievement and English Achievement. Three of the models for the colleges is similar to the previous model. Only the College of Agricultural Sciences model is similar to the one used by the UPR System. In general, the analysis suggests that more than three variables should be used in order to improve the prediction ability (Cp). It is necessary to incorporate other additional variables in the model since the percentage of the variability explained by the models is low (but comparable to similar studies). For example, the number of credits in key courses (e.g science and math) taken in high school could be a variable to be considered.

38. Future studies Use other modeling techniques, Regression Trees, Logistic Models, Hierarchical Analysis Study extraordinary and worrisome cases in order to understand the patterns in this cases (examples will be presented herein) Consider other measurements of performance (e.g. ratio of approved credits against credits taken, binary model of obtaining a degree or not, among others)

39. Future Studies

40. Scheme of Future Studies GPA 1 st Year GPA Expected Unexpected Analysis by Quadrants for Future Studies Extraordinary Cases Worrisome Cases

45. Extraordinary Cases

49. Additional information Contact us at: –antonio@uprm.eduantonio@uprm.edu –davidg@ece.uprm.edudavidg@ece.uprm.edu Download this presentation at: http://oiip.uprm.edu/pres.html