Using data mining techniques to predict students at risk of poor performance

Alharbi, Zahyah, Cornford, James, Dolder, Liam and De La Iglesia, Beatriz (2016) Using data mining techniques to predict students at risk of poor performance. In: SAI Computing Conference (SAI), 2016. IEEE Press, GBR. ISBN 9781467384612

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Abstract

The achievement of good honours in Undergraduate degrees is important in the context of Higher Education (HE), both for students and for the institutions that host them. In this paper, we look at whether data mining can be used to highlight performance problems early on and propose remedial actions. Furthermore, some of the methods may also form the basis for recommender systems that may guide students towards their module choices to increase their chances of a good outcome. We use data collected through the admission process and through the students' degrees. In this paper, we predict good honours outcomes based on data at admission and on the first year module results. To validate the proposed results, we evaluate data relating to students with different characteristics from different schools. The analysis is achieved by using historical data from the Data Warehouse of a specific University. The methods used, however, are fairly general and can be used in any HE institution. Our results highlight groups of students at considerable risk of obtaining poor outcomes. For example, using admissions and first year module performance data we can isolate groups for one of the studied schools in which only 24% of students achieve good honour degrees. Over 67% of all low achievers in the school can be identified within this group.

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences
Faculty of Social Sciences > Norwich Business School
Depositing User: Pure Connector
Date Deposited: 24 Sep 2016 01:01
Last Modified: 19 Mar 2020 02:15
URI: https://ueaeprints.uea.ac.uk/id/eprint/60353
DOI: 10.1109/SAI.2016.7556030

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