Predicting Academic Performance and
Attrition in Undergraduate Students
María Pita Carranza
Ángel Centeno
Ángela Corengia
Laura Llull
Belén Mesurado
Cecilia Primogerio
Francisco Redelico
INTRODUCTION
Improvement of EDUCATIONAL QUALITY
Matter of concern to all Higher Education Institutions
Develop
TOOLS to predict to what extent students are capable to:
- Reach a good academic performance
- Finish their studies successfully
PURPOSE
Explore the relationship between
ACADEMIC
PERFORMANCE
EDUCATIONAL
APTITUDES
(DAT)
ATTRITION
1530 undergraduate students
from 8 different programmes of
a private university in Argentina
-
Accounting / Business Economics
Social Communication
Industrial Engineering / Software Engineering
Law
Medicine
Nursing
DAT
DIFFERENTIAL APTITUDE TEST
Set of tests that “measure” different
Educational Aptitudes
- Abstract reasoning
- Verbal reasoning
- Speed and accurancy
- Language / Spelling
- Numerical ability
- Space relations
- Mechanical reasoning
Complete set defines a cognitive profile
for each student
Why DAT?
(Bennet, Seashore, Wesman, Justo)
VALIDITY
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.
DAT has a high enough reliability and a sufficiently low
intercorrelation as to be considered a battery of tests with
a good discriminative power.
THEORETICAL FRAMEWORK
Review and synthesis of published studies
INTERNATIONAL
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.
ARGENTINA
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.
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.
METHOD
Relationship between
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
METHOD
SAMPLE
1530 first year undergraduate students from of a
private university in Argentina
- 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
METHOD
1. Exploratory analysis of data.
2. General linear model: educational aptitudes
related to students’ academic performance.
3. Multiple regressions: relationship of each
educational aptitude with academic performance.
4. Generalized linear model: relationship between
educational aptitudes and attrition.
RESULTS
Regression Model for each Course
p-value (< .05)
Program
R2
AR
VR
S&A
NA
L
S
MR
SR
Nursing
.01
.001
.05
.001
-
-
-
-
.34
Social
Communication
.01
.0001
.0001
-
.0001
.0001
-
.001
.25
Law
.001
.000
.01
.05
.000
.000
.05
-
.25
Engineering
.01
.000
.01
.01
.001
.001
-
-
.15
Business
.01
.0001
.05
.05
.01
.01
-
-
.14
Medicine
.05
.000
-
.000
.01
.001
-
-
.12
Source: Made by the authors
RESULTS
Odds Ratio and Grade of significance
Program
Odds ratio
Grade of
significance
Nursing
1.14
.45
1.2
.10
1.82
.10
2
.10
Business
2.14
.10
Medicine
1.4
.40
Social
Communication
Law
Engineering
Source: Made by the authors
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.
CONCLUSION
Population enrolled uniform in
age
socio-cultural background
economic background
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 scores obtained have allowed designing personalized
strategies of mentoring in order to promote good academic
performance and to increase retention rates.
Predicting Academic Performance and Attrition
in Undergraduate Students
THANK YOU!!!
mpita@austral.edu.ar