The Relationship between the Admission Index and the Student Success at the University of Puerto Rico at MayagezDr. David Gonzlez BarretoDr. Antonio A. Gonzlez QuevedoOffice of Institutional Research and PlanningUniversity of Puerto Rico at Mayagez

Presented at the AIR Forum 2005San Diego, CaliforniaJune 1, 2005

Background InformationUniversity of Puerto Rico at Mayagez (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.

ObjectivesShow the profile of incoming freshmen from 1990-2003 at the University of Puerto Rico at Mayagez:Admission index (AI)Type of high schoolGenderHigh 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.

Outline of the PresentationIncoming freshmen profile Prediction modelsComparison of modelsConclusions and recommendationsFuture studies

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.

Profile of incoming Freshmen

Incoming Freshmen by Type of School

Incoming Freshmen by Gender

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 1995The index includes three components: the high school grade point average, College Entrance Examination Board (CEEB) score for Verbal Aptitude (Spanish), CEEB score for Mathematical AptitudeThe 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.

Average Admission Index by Year

Average Admission Index by Type of School

Average Admission Index per Year - Arts

Average Admission Index per Year - Sciences

Average Admission Index per Year Agricultural Sciences

Average Admission Index per Year Business Administration

Average Admission Index per Year Engineering

Average GPA per Type of School

HS and 1st Year GPAs per Type of School

Math Aptitude per Type of School

Average Verbal Aptitude per Type of School

Average Spanish Achievement per Type of School

Average Mathematical Achievement per Type of School

Average English Achievement per Type of School

Summary of Incoming Students ProfileAverage 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.

Comparison with USA Trends1Shift 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.

Comparison with USA Trends (continued)In addition to GPA, other admission factors in order of importance are:ACT or SAT I scoresPattern of high school courseworkLetters of recommendations and essays (more prevalent in private institutionsInterviewsCalifornia has recently proposed that aptitude test scores be replaced by achievement test scores

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

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

Prediction Models

Prediction Models

Best Subsets Methods All Colleges

Best Subsets Methods College of Engineering

Best Subsets Methods Arts and Sciences : Sciences

Best Subsets Methods Arts and Sciences : Arts

Best Subsets Methods College of Agricultural Sciences

Best Subsets Methods College of Business Administration

Summary of comparison of modelsThe model with three variables that best predicts 1st 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.

Future studies

Use other modeling techniques, Regression Trees, Logistic Models, Hierarchical AnalysisStudy 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)

Future Studies

Scheme of Future StudiesGPA1st Year GPAExpectedUnexpectedAnalysis by Quadrants for Future StudiesExtraordinary CasesWorrisome Cases

Worrisome Cases

Worrisome Cases

Worrisome Cases

Worrisome Cases

Extraordinary Cases

Extraordinary Cases

Extraordinary Cases

Extraordinary Cases

Additional informationContact us at:antonio@uprm.edudavidg@ece.uprm.eduDownload this presentation at:http://oiip.uprm.edu/pres.html