1. María Pita Carranza Ángel Centeno Ángela Corengia Laura Llull Belén Mesurado Cecilia Primogerio Francisco Redelico Predicting Academic Performance and Attrition in Undergraduate Students

2. INTRODUCTION EDUCATIONAL QUALITY Improvement of EDUCATIONAL QUALITY Matter of concern to all Higher Education Institutions TOOLS Develop TOOLS to predict to what extent students are capable to: - Reach a good academic performance - Finish their studies successfully

3. Explore the relationship between PURPOSE EDUCATIONAL APTITUDES (DAT) ACADEMIC PERFORMANCE 1530 undergraduate students from 8 different programmes of a private university in Argentina ATTRITION - Accounting / Business Economics - Social Communication - Industrial Engineering / Software Engineering - Law - Medicine - Nursing

4. DAT DIFFERENTIAL APTITUDE TEST Set of tests that “measure” different Educational Aptitudes Educational Aptitudes Complete set defines a cognitive profile for each student - Abstract reasoning - Verbal reasoning - Speed and accurancy - Language / Spelling - Numerical ability - Space relations - Mechanical reasoning

5. Ability to predict the success or future performance in certain activities. RELIABILITY Tests are consistent, the results obtained are stable, free of casual failures. INDEPENDENCE OF MEASURED APTITUDES Tests show low intercorrelation. The measured aptitudes of the different tests differ enough to justify the inclusion of all tests in the series. This is specially satisfactory if it is considered that each test was devised to have its own validity. VALIDITY Why DAT? (Bennet, Seashore, Wesman, Justo) DAT has a high enough reliability and a sufficiently low intercorrelation as to be considered a battery of tests with a good discriminative power.

6. THEORETICAL FRAMEWORK Review and synthesis of published studies ARGENTINAINTERNATIONAL The results of the standardized test scores are related to students’ academic performance, among other indicators, especially during the first year of the undergraduate courses. Although it is difficult to find studies related to results of standardized tests, institutions share the same concern about the search of indicators: The studies surveyed are related to: - socio-demographical variables - school background - performance in admission process - job situation - professional insertion expectations - personality, problem-solving and intelligence tests, etc.

7. RELEVANCE Provide information to academic advisers. Early detection of students that are potentially vulnerable to suffer academic failure. Provide empiric evidence to theoretical discussion about this subject.

8. METHOD EDUCATIONAL APTITUDES DAT - Abstract reasoning - Verbal reasoning - Speed and accurancy - Language / Spelling - Numerical ability - Space relations - Mechanical reasoning ACADEMIC PERFORMANCE GPA Grade Point Average of the first academic year ATTRITION Student drops out studies Relationship between

9. METHOD 1530 first year undergraduate students from of a private university in Argentina SAMPLE - 8 programmes: Business -Accounting and Business Economics-, Social Communication, Engineering -Industrial Engineering, Software Engineering-, Law, Medicine and Nursing. - Age: 17 to 20 years old - Socio-economic level: medium to medium-high sectors - Enrolled in 2002, 2003, 2004 and 2005

10. METHOD Exploratory analysis 1. Exploratory analysis of data. General linear model 2. General linear model : educational aptitudes related to students’ academic performance. Multiple regressions 3. Multiple regressions : relationship of each educational aptitude with academic performance. Generalized linear model 4. Generalized linear model : relationship between educational aptitudes and attrition.

11. Regression Model for each Course Source: Made by the authors Program p-value (<.05) R2R2 ARVRS&ANALSMRSR Nursing.01.001.05.001----.34 Social Communication.01.0001 - -.001.25 Law.001.000.01.05.000.05-.25 Engineering.01.000.01.001 --.15 Business.01.0001.05.01 --.14 Medicine.05.000-.01.001--.12 RESULTS

12. RESULTS ProgramOdds ratio Grade of significance Nursing 1.14.45 Social Communication 1.2.10 Law 1.82.10 Engineering 2.10 Business 2.14.10 Medicine 1.4.40 Odds Ratio and Grade of significance Source: Made by the authors

13. CONCLUSION DAT scores: Allows estimating students’ academic performance in the first year of undergraduate programs. Predict moderately chances of attrition in some programmes -Business, Engineering, Law and Social Communication-, whereas in others -Nursing and Medicine- its prediction capacity is not significantly, in the statistical meaning.

14. Measure the impact of other variables -motivation, satisfaction, stress- in order to complement this study with other factors that can influence both academic performance and retention. DAT DAT scores obtained have allowed designing personalized strategies of mentoring in order to promote good academic performance and to increase retention rates. Population enrolled uniform in age socio-cultural background economic background CONCLUSION

15. THANK YOU!!! mpita@austral.edu.ar Predicting Academic Performance and Attrition in Undergraduate Students