Feedback through critical indicators of student performance: contributing to the assessment of high school education David González-Barreto, PhD Antonio González-Quevedo, PhD Sonia Bartolomei-Suárez, PhD University of Puerto Rico – Mayagüez

June 24th, ASEE Annual Conference & Exposition 2 Outline  Summary  Background  Introduction and Justification  Presentation of the Model  Benefits of the Model  Description and Components of the Project  Evaluation of Two High Schools using a Subgroup of Critical Indicators  Conclusions  Acknowledgements  Bibliography

June 24th, ASEE Annual Conference & Exposition 3 Summary  Data kept at the Office of Institutional Research and Planning  Students’ performance in their high schools  Students’ performance at the university level  Model of feedback mechanism to high schools  Critical indicators  High school GPA  Ratio between students applying and students admitted  Retention rates  Graduation rates  Grades obtained in general education courses at the university (e.g., Mathematics, English and Spanish)  Expected outcomes for the university and the high schools

June 24th, ASEE Annual Conference & Exposition 4 Background  Office of Institutional Research and Planning (OIRP) at the University of Puerto Rico at Mayagüez (UPRM)  Limitation: variables not studied (1, 2)  High schools ranking  Quality of high schools  Pre-college preparation  Recruitment programs  Admission policy  Financial assistance  Academic intervention programs  Previous work (3)

June 24th, ASEE Annual Conference & Exposition 5 Introduction and Justification Information available in our data warehouse  High school grade point average  Scores in College Entrance Examination Board (CEEB) tests (aptitude in Math, Spanish and English, and achievement in the same areas)  Grade point average at the university level and at the specialty level  Grades obtained in the college courses

June 24th, ASEE Annual Conference & Exposition 6 High School UPRM Graduate School CI Academic Performance Admitted/ Applicants First Year GPA Graduation GPA Retention Index Graduation Rates Courses Averages  Spanish  English  Mathematics Others CI Academic Performance Admitted/ Applicants First Year GPA Graduation GPA Retention Index Graduation Rates Courses Averages  Spanish  English  Mathematics Others CI Academic Performance High School GPA CEEB Results Courses Averages  Spanish  English  Mathematics Others CI Academic Performance High School GPA CEEB Results Courses Averages  Spanish  English  Mathematics Others ……… Supplier and Receptor Model of our Academic Performance Model with Critical Indicators

June 24th, ASEE Annual Conference & Exposition 7 Potential Benefits of the Model  Positive influence on high schools whose students attend our university  Indicators will allow high school to assess their performance  Collaboration between high schools and the university can be established to established better teaching techniques and share information on the preparation expected for the university courses from the perspective of the high schools  The indicators can be developed based on geographic location of the high schools, gender and public or private system

June 24th, ASEE Annual Conference & Exposition 8 Marginal Distributions of High School GPAs and First Year GPAs at UPRM

June 24th, ASEE Annual Conference & Exposition 9 Marginal Distributions of High School GPAs and First Year GPAs at UPRM for two hypothetical high schools

June 24th, ASEE Annual Conference & Exposition 10 Description of the Components of the Project  Potential users of the model  Parents of our applicants  Department of Education of the Commonwealth of Puerto Rico  Public and private school teachers  High school principals and counselors  Our Admissions Office  Eventual availability of the model and the results in our site:

June 24th, ASEE Annual Conference & Exposition 11 Evaluation of the two high schools using a subgroup of the critical indicators Evaluation based on five critical indicators  Retention rate (students staying for their second year of studies who entered our institution as first year students)  Graduation rate (the US Department of Education definition)  GPA in Math courses at the university  GPA in Spanish courses at the university  GPA in English courses at the university

June 24th, ASEE Annual Conference & Exposition 12 High school A with performance above the median

June 24th, ASEE Annual Conference & Exposition 13 High school B with performance below the median

June 24th, ASEE Annual Conference & Exposition 14 Conclusions  This system will prove to be essential for the assessment of our public and private high schools  Each school can use this system to assess the quality of their teaching as far a preparing the students for the university  This system can be extended to assess the performance of universities that send us graduate students

June 24th, ASEE Annual Conference & Exposition 15 Acknowledgments  Our former Institutional Researcher Irmannette Torres-Lugo, M.S.  Our Institutional Researcher and Database Administrator, engineer Leo I. Vélez, M.S.  Our Webmaster and Programmer Maira Rodríguez, B.S.

June 24th, ASEE Annual Conference & Exposition 16 Bibliography 1.Mo-Yin S Tam, Uday Sukhatme, “The importance of high school quality in university admissions decisions,” College and University Winter 2003, 78(3), pp Gary S May, Daryl E Chubin, “A retrospective on undergraduate engineering success for underrepresented minority students,” Journal of Engineering Education, January 2003, 92(1), pp Bartolomei-Suárez, Sonia M., González-Barreto, D. and González-Quevedo, A.A., “Using an Expected Loss Function to Identify Best High Schools for Recruitment,” Proceedings of the 10th International Conference on Engineering Education, Sept. 3- 7, 2007, Coimbra, Portugal.