Electronic Preparatory Test for Mathematics Undergraduates: Implementation, Results and Correlations

Siri Chongchitnan

Abstract


We present a study of the implementation of the Electronic Preparatory Test for beginning undergraduates reading mathematics at the University of Hull. The Test comprises two elements: diagnostic and self-learning. The diagnostic element identifies gaps in the background knowledge, whilst the self-learning element guides students through an upcoming material. The Test lends itself to an early identification of weak and strong students coming from a wide range of background, allowing follow-ups to be made on a topic-specific basis. The results from the Tests, collected over three years, correlate positively with end-of-year examination results. We show that such a Preparatory Test can be a better predictor of success in the first-year examination in comparison with university entry qualifications alone.


Keywords


Diagnostic; Electronic assessments; Transition.

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References


Appleby, J., Samuels, P. and Treasure-Jones, T., 1997. Diagnosys—a knowledge-based diagnostic test of basic mathematical skills. Computers & Education, 28, pp. 113-131.

https://doi.org/10.1016/S0360-1315(97)00001-8

Dobson, J. L., 2008. The use of formative online quizzes to enhance class preparation and scores on summative exams. Advances in Physiology Education, 32, 4, pp. 297-302.

https://doi.org/10.1152/advan.90162.2008

Edwards, P., 1997. Just how effective is the mathematics diagnostic test and follow-up support combination? Teaching Mathematics and its Applications: An International Journal of the IMA, 16, pp. 118-121.

https://doi.org/10.1093/teamat/16.3.118

Freeman, S., O'Connor, E., Parks, J. W., Cunningham, M., Hurley, D., Haak, D., Dirks, C., Wenderoth, M. P., 2007. Prescribed active learning increases performance in introductory biology. CBE Life Science Education, 6, pp. 132-139.

https://doi.org/10.1187/cbe.06-09-0194

Gillard, J., Levi, M. and Wilson, R., 2010. Diagnostic testing at UK universities: an e-mail survey. Teaching Mathematics and its Applications: An International Journal of the IMA, 29, pp. 69-75.

https://doi.org/10.1093/teamat/hrq004

Lee, S., Harrison, M. C., Pell, G. and Robinson, C. L., 2008. Predicting performance of first year engineering students and the importance of assessment tools therein. Engineering Education, 3, pp. 44-51.

https://doi.org/10.11120/ened.2008.03010044

Moravec, M., Williams, A., Aguilar-Roca, A., and O'Dowd, D. K., 2010. Learn before Lecture: A Strategy That Improves Learning Outcomes in a Large Introductory Biology Class. CBE Life Sciences Education, 9, 4, pp. 473-481.

https://doi.org/10.1187/cbe.10-04-0063

Parsons, S., Croft, T. and Harrison, M., 2009. Does students’ confidence in their ability in mathematics matter? Teaching Mathematics and its Applications: An International Journal of the IMA, 28, pp. 53-68.

https://doi.org/10.1093/teamat/hrp010

Sangwin, C., 2015. Computer Aided Assessment of Mathematics Using STACK. Cham: Springer International Publishing, pp. 695-713.

https://doi.org/10.1007/978-3-319-17187-6_39

Yates, J. and James, D., 2006. Predicting the ‘strugglers’: a case-control study of students at Nottingham university medical school. BMJ, 332, pp. 1009-1013.

https://doi.org/10.1136/bmj.38730.678310.63




DOI: https://doi.org/10.21100/msor.v17i3.1014

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