de Andrade, Matheus O;
Zurita, Mariana;
Burova, Iva;
Nyamapfene, Abel;
(2022)
Assessing higher levels of learning through real-life problems in engineering mathematics.
In: Kallel, I and Kammoun, HM and Akkari, A and Hsairi, L, (eds.)
Proceedings of the 2022 IEEE Global Engineering Education Conference (EDUCON 2022).
(pp. pp. 2002-2006).
IEEE: Tunis, Tunisia.
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Abstract
This paper discusses the design of engineering mathematics assessment that encourages learning beyond algorithmic recall. Our approach is based on the MATH (mathematical assessment task hierarchy) taxonomy. We propose using mathematics as a tool to analyse relatable problems and produce clear engineering deliverables to ensure that academic knowledge is translated to real-life situations. Creating engineering scenarios was instrumental in fostering active engagement, enquiry, creativity and reducing opportunities for academic misconduct in our first-year engineering mathematics assessments. An example of an exam question covering the topics of calculus, linear algebra, and dimensional analysis is given to illustrate the concepts discussed. Qualitative feedback from 203 students on an assessment paper containing the question shown herein is also included. Our data shows that most students had little to no previous exposure to real-world questions and that most of their time engaging with the assessment was spent critically analysing the problems. In addition, student feedback showed that contextual assessment is perceived as more challenging and exciting than pure mathematics problems and that students believe contextual assessment adds value to their education.
| Type: | Proceedings paper |
|---|---|
| Title: | Assessing higher levels of learning through real-life problems in engineering mathematics |
| Event: | 13th IEEE Global Engineering Education Conference (IEEE EDUCON) |
| Location: | Gammarth, TUNISIA |
| Dates: | 28 Mar 2022 - 31 Mar 2022 |
| ISBN-13: | 9781665444347 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/EDUCON52537.2022.9766484 |
| Publisher version: | https://doi.org/10.1109/EDUCON52537.2022.9766484 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Social Sciences, Science & Technology, Technology, Education, Scientific Disciplines, Engineering, Multidisciplinary, Education & Educational Research, Engineering, Engineering mathematics, contextual learning, assessment, deep learning, higher level of learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Education UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Education, Practice and Society UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Engineering Science Faculty Office |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10161558 |
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