MSSTL10-Carlow IT May
2010
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Setting the scene
Initial phase of research
Aim of presentation
Profiling at risk students
Predicting failure of at risk students
Conclusions
Implications for future work
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Data collected on Technology and Science students since 1997
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Up to 600 students tested each year
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It currently consists of information on almost 7000 students
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The dataset contains information on students such as:
 Gender
 Leaving Cert. mathematics Grade, Level and Points
 Degree programme of study
 Attendance at support tutorials
 Performance in service mathematics examinations
 Standard or Non standard
 Performance in the diagnostic test
Numbers at risk of failing service mathematics?
1998
2008
Technology
% at risk
32.8%
46.4%
(% with HL)
41.0%
33.0%
Science
%at risk
21.3%
46%
(% with HL)
55.0%
38.0%
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Decline in mathematical competencies between
1997-2008 evident (Gill et al., 2010)
Investigation in changes in competencies between
1997-2008 by Leaving Certificate grade(Faulkner et
al., 2010)
80
70
Mean percentage correct
60
50
HC1
OA1
40
OA2
OB1
30
OB3
20
10
0
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Year
Mean diagnostic test score (expressed as a percentage of
correct answers out of 40 questions) from 1998 to 2008 for
all students with grades HC1, OA1, OA2, OB1 and
OB3(Faulkner et al 2010).
Whole Cohort
Year
1998
2008
% doing HL
237
(46.7%)
239
(35.3%)
% doing OL
266
(52.5%)
381
(56.3%)
Non Standard
students
4
(0.8%)
57
(8.4%)
Total
507
(100%)
677
(100%)
To use information on students within the
database such as gender, Leaving
Certificate points, diagnostic test result
etc. to build a predictive model of
success/failure
Science maths 1
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Mean CAO maths points 55.3
53.3
54.1
2006
2007
2008
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Statistically significant associations were found
between success/failure in Science maths 1 and
- CAO maths points
- Leaving Certificate Level and Grade
- Mean Diagnostic Test results
- Students who sat the diagnostic
test/did not sit the Diagnostic test
Technology maths 1
 Mean CAO maths points 55.1
55.5
54.2
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2006
2007
2008
Statistically significant associations were found between
success/failure in Technology maths 1 and:
- CAO maths points
- Leaving Certificate Level and Grade
- Diagnostic Test result
- Students who sat the diagnostic
test/did not sit the Diagnostic test
Discriminant Function Analysis
Why use Discriminant Analysis?
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It is common practice to use discriminant analysis where there are
just two populations
The discriminant function analysis can act as a tool for classifying
future students
The nature of discriminant analysis i.e. its ability to determine
what variables have a relationship with performance and
categorise students accordingly is of great benefit to the design,
implementation and evaluation of any educational program/policy
(Thomas et al 1996)
Dataset 1. The Technological 2006-2008
Z = 0.059(Leaving Cert. Maths Points) +
0.065(Diagnostic Test Result)
where C= 4.3
Z ≥4.3
Z ≤ 4.3
classified as a success
classified as a failure.
Technology
Science
% of correctly classified
success cases
66.2%
60.3%
% of correctly classified
failure cases
83.2%
78.0%
Overall
69.7%
62.9%
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Function’s ability to predict failure in 2009
Science and Technology cohorts?
Technological Discriminant Function will be used
to identify the at risk students entering UL in
the academic year 2010/11
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Profiling at risk students between 2006-2008
Ordinary Level Leaving Certificate mathematics
grade
Identified as at risk by the diagnostic test or
Have not sat the diagnostic test
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Predicting failure in service mathematics
Discriminant Analysis
Technology 2006-2008 function:
Z = 0.059(Leaving Cert. Maths Points) +
0.065(Diagnostic Test Result)
where C= 4.3
The discriminant function produced in this phase
of the research will allow for
The identification of at risk students in the
academic year 2010/11 in the first week of term
The design of a targeted intervention strategy for
the identified at risk students