PREDICTION OF ENGINEERING STUDENT PROGRESSION FROM ENTRANCE DATA
Citation:
Kevin Kelly & Claire Marshall, PREDICTION OF ENGINEERING STUDENT PROGRESSION FROM ENTRANCE DATA, 29th International Manufacturing Conference, Belfast, Ireland, 29-30 August 2012, 2012Download Item:
Abstract:
Most Western economies are suffering from problems in attracting students
to study Science, Engineering and T
echnology courses at university level.
Concurrently, engineering programmes te
nd to have higher dropout rates than
courses in humanities, business or health
sciences. They also typically have a
higher unit cost per student. Many reasons
have been suggested in the literature
why the retention rates are lower in such
programmes. However little data exists
in the Irish context for what information is
useful in identifying which factors are
predictive of retention and to what degr
ee they influence retention probability.
What information is available, typically considers single factors (e.g.
mathematical attainment, overall grades,
English attainment etc), but does not
consider any interaction effects.
The work reported in this paper examines entrance data for approximately
22,000 students in Trinity College over a 10 year period. Those variables which
are predictive of retention are identifie
d – both for engineers and for students
generally. It is shown that appropriate u
se of this information provides significant
extra discrimination (over either random sel
ection or any single factor model) in
identifying those students most likely to encounter progression difficulties.
Sponsor
Grant Number
European Commission
505330-LLP-1-2009-1-SE-KA1-KA1SCR
Author's Homepage:
http://people.tcd.ie/kekellyDescription:
PUBLISHEDBelfast, Ireland
Author: KELLY, KEVIN
Other Titles:
29th International Manufacturing ConferenceType of material:
Conference PaperAvailability:
Full text availableKeywords:
STUDENT PERFORMANCE, EDUCATION, RETENTIONLicences: