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

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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 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

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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 AM ranked number 1 with 6,411 students
  (ASEE Prism, Summer 2004).
• Our college granted 680 bachelors degrees in
  2001-2002, 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 bachelors degrees awarded (ASEE Prism,
  December 2003).

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Objectives

• Show the profile of incoming engineering 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
• 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.

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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

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Profile Mean HS GPA by School Type
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Profile Mean Verbal Aptitude by Type of School
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Profile Mean Math Aptitude by Type of School
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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.

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Mean AI by Type of School
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Average Admission Index per Year Engineering
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HS and 1st Year GPAs per Type of School
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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 3.86.
• 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.

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Comparison with USA Trends1

• 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.
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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
• 1st Year GPA f(GPA, Verbal Aptitude,
  Mathematical Aptitude, English Achievement,
  Mathematical Achievement, Spanish Achievement)
  e

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Prediction Models
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Prediction Models
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Best Subsets Methods College of Engineering
              Vars               R-Sq(adj)             Mallows C-p           G P A A P T _ V E R B A P T _ M A T E   A C H _ I N G   A C H _ M A T   A C H _ E S P
1 11.5 1743.4 X          
2 19.5 618.1 X       X  
3 21.6 324.2 X     X X  
4 22.8 165.9 X     X X X
5 23.7 37.5 X   X X X X
6 23.9 7 X X X X X X
Actual 20.8 438.0 X X X
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Summary of comparison of models

• The model with three variables that best predicts
  1st 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.

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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. 187-207.
• Wilson, Kenneth M. (1983). A Review of Research
  on the Prediction of Academic Performance after
  the Freshman Year. College Board Report No. 83-2.

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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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Additional information

• Contact us at
• antonio_at_uprm.edu
• davidg_at_ece.uprm.edu
• Download this presentation at
• http//oiip.uprm.edu/pres.html