Predicting performance of 1st year engineering students and the importance of assessment tools therein

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.