MSSTL10-Carlow IT May 2010

1. Fiona Faulkner‘Predicting failure in service mathematics in the University of Limerick’Supervisors:Dr. AilishHannigan and Dr. Olivia Gill MSSTL10-Carlow IT May 2010

2. Presentation Overview • 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

3. Setting the Scene • Data collected on Technology and Science students since 1997 • Up to 600 students tested each year • It currently consists of information on almost 7000students • 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?

5. Initial Phase of research • 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)

6. 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).

8. Overall Aim 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

9. Profiling at risk students(2006-2008) Science maths 1 • Mean CAO maths points 55.3 2006 53.3 2007 54.1 2008 • 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

10. Technology maths 1 • Mean CAO maths points 55.1 2006 55.5 2007 54.2 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

11. Predicting Failure of at risk students Discriminant Function Analysis Why use Discriminant Analysis? • 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)

12. Outcome of Discriminant Analysis 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 classified as a success Z ≤ 4.3 classified as a failure.

13. Ability of each function to classify failure: • Function’s ability to predict failure in 2009 Science and Technology cohorts?

14. Result Technological Discriminant Function will be used to identify the at risk students entering UL in the academic year 2010/11

15. Conclusion • 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

16. Conclusion contd. • 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

17. Implications for future work 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

18. Thank you