Predicting performance of first year engineering students and the importance of assessment tools therein

dc.creator	Stephen Lee
dc.creator	Martin Harrison
dc.creator	Godfrey Pell
dc.creator	Carol Robinson
dc.date	2008-06-01T00:00:00Z
dc.date.accessioned	2015-07-20T22:17:04Z
dc.date.available	2015-07-20T22:17:04Z
dc.identifier	1750-0044
dc.identifier	1750-0052
dc.identifier	https://doaj.org/article/c41894981c5445f5b4080a94b17b5ed3
dc.identifier.uri	http://evidence.thinkportal.org/handle/123456789/18853
dc.description	In 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.
dc.language	English
dc.publisher	The Higher Education Academy Engineering Subject Centre
dc.relation	http://www.engsc.ac.uk/journal/index.php/ee/article/viewFile/75/115
dc.relation	https://doaj.org/toc/1750-0044
dc.relation	https://doaj.org/toc/1750-0052
dc.rights	CC BY-NC-ND
dc.source	Engineering Education, Vol 3, Iss 1, Pp 44-51 (2008)
dc.subject	engineering mathematics
dc.subject	mathematics diagnostic testing
dc.subject	Education (General)
dc.subject	L7-991
dc.subject	Education
dc.subject	L
dc.subject	DOAJ:Education
dc.subject	DOAJ:Social Sciences
dc.subject	Engineering (General). Civil engineering (General)
dc.subject	TA1-2040
dc.subject	Technology
dc.subject	T
dc.subject	DOAJ:General and Civil Engineering
dc.subject	DOAJ:Technology and Engineering
dc.subject	Education (General)
dc.subject	L7-991
dc.subject	Education
dc.subject	L
dc.subject	DOAJ:Education
dc.subject	DOAJ:Social Sciences
dc.subject	Engineering (General). Civil engineering (General)
dc.subject	TA1-2040
dc.subject	Technology
dc.subject	T
dc.subject	DOAJ:General and Civil Engineering
dc.subject	DOAJ:Technology and Engineering
dc.subject	Education (General)
dc.subject	L7-991
dc.subject	Education
dc.subject	L
dc.subject	Engineering (General). Civil engineering (General)
dc.subject	TA1-2040
dc.subject	Technology
dc.subject	T
dc.subject	Education (General)
dc.subject	L7-991
dc.subject	Education
dc.subject	L
dc.subject	Engineering (General). Civil engineering (General)
dc.subject	TA1-2040
dc.subject	Technology
dc.subject	T
dc.subject	Education (General)
dc.subject	L7-991
dc.subject	Education
dc.subject	L
dc.subject	Engineering (General). Civil engineering (General)
dc.subject	TA1-2040
dc.subject	Technology
dc.subject	T
dc.title	Predicting performance of first year engineering students and the importance of assessment tools therein
dc.type	article