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Student profile of the incoming First Year Class of the College of Engineering at UPRM and their academic performance after their first year Dr. David.

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Presentation on theme: "Student profile of the incoming First Year Class of the College of Engineering at UPRM and their academic performance after their first year Dr. David."— Presentation transcript:

1 Student profile of the incoming First Year Class of the College of Engineering at UPRM and their academic performance after their first year Dr. David González Barreto Dr. Antonio A. González Quevedo Office of Institutional Research and Planning University of Puerto Rico at Mayagüez Presented at 2005 ASEE Annual Conference Portland, Oregon June 13, 2005

2 Background Information for the College of Engineering In 2003, the College of Engineering of the University of Puerto Rico at Mayagüez had an undergraduate enrollment of 4,476. This enrollment places our college in the 13th position of United States of America Engineering Schools. Texas A&M ranked number 1 with 6,411 students (ASEE Prism, Summer 2004). Our college granted 680 bachelor’s degrees in , ranking number 1 in the degrees granted to Hispanics. The second spot belonged to Polytechnic University of Puerto Rico with 305 degrees, and the third to Florida International University with 154 bachelor’s degrees awarded (ASEE Prism, December 2003).

3 Objectives Show the profile of incoming engineering freshmen from 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 A comparison between actual admission criteria and suggested alternative criteria is also presented. This longitudinal comparison is carried out to evaluate proposed changes in admission criteria in the future.

4 Outline of the Presentation Profile of the Incoming First Year Engineering Classes Description of Admission Criteria Performance of the students after their First Year in College Suggested Admission Criteria Findings and Conclusions Bibliography Acknowledgements

5 Profile: Mean HS GPA by School Type

6 Profile: Mean Verbal Aptitude by Type of School

7 Profile: Mean Math Aptitude by Type of School

8 Description of Admission Criteria The Admission Index (IGS) 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.

9 Mean AI by Type of School

10 Average Admission Index per Year – Engineering

11 HS and 1 st Year GPAs per Type of School

12 Summary of Incoming Students Profile The average entering class of engineering is 761 students, of which 62% are male and 38% is female Average high school grade point average is higher for public schools students, 3.84/4.0 when compared to private schools students who average 3.79/4.0. The average GPA has increased for the 14 years of study from 3.67 to 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.

13 Comparison with USA Trends 1 –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 –California has recently proposed that aptitude test scores be replaced by achievement test scores 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.

14 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

15 Prediction Models

16

17 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 X X X X XX X XXX X XXXX XXXXXX Actual XXX

18 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. 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.

19 Bibliography ASEE. (2004). Prism. “Databytes”. Page 24. Summer. ASEE. (2004). Prism. “Databytes”. Page 19. December. MINITAB® Release 14. (2004). Minitab Inc. State College, PA. Montgomery, Douglas C., Peck, Elizabeth A., and Vining, Geoffrey G. (2003). Introduction to Linear Regression Analysis. Wiley and Sons, New York. Pike, Gary R. and Saupe, Joseph L. (2002). “Does High School Matter? An Analysis of Three Methods of Predicting First-Year Grades.” Research in Higher Education. 43(2), pp Wilson, Kenneth M. (1983). A Review of Research on the Prediction of Academic Performance after the Freshman Year. College Board Report No

20 Acknowledgements The authors want to acknowledge the effort by Leo I. Vélez and Irmannette Torres from the Office of Institutional Research and Planning of the University of Puerto Rico at Mayagüez for providing and validating the data used in this study.

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