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Predicting performance of 1st year engineering students and the importance of assessment tools therein
journal contribution
posted on 2011-09-06, 11:45 authored by Stephen Lee, Martin C. Harrison, Godfrey Pell, Carol RobinsonIn recent years, the increase in the
number of people entering university
has contributed to a greater variability
in the background of those beginning
programmes. Consequently, it has become
even more important to understand a
student’s prior knowledge of a given
subject. Two main reasons for this are to
produce a suitable first year curriculum
and to ascertain whether a student would
benefit from additional support. Therefore,
in order that any necessary steps can be
taken to improve a student’s performance,
the ultimate goal would be the ability to
predict future performance.
A continuing change in students’ prior
mathematics (and mechanics) knowledge
is being seen in engineering, a subject
that requires a significant amount of
mathematics knowledge. This paper
describes statistical regression models
used for predicting students’ first year
performance. Results from these models
highlight that a mathematics diagnostic test
is not only useful for gaining information
on a student’s prior knowledge but is
also one of the best predictors of future
performance. In the models, it was also
found that students’ marks could be
improved by seeking help in the university’s
mathematics learning support centre.
Tools and methodologies (e.g. surveys and
diagnostic tests) suitable for obtaining data
used in the regression models are also
discussed.
History
School
- Science
Department
- Mathematics Education Centre
Citation
LEE, S. ... et al, 2008. Predicting performance of 1st year engineering students and the importance of assessment tools therein. Engineering Education, 3 (1), pp. 44-51Publisher
Higher Education Academy © The authorsVersion
- VoR (Version of Record)
Publication date
2008Notes
This article was published in the journal, Engineering Education.ISSN
1750-0052Publisher version
Language
- en