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

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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.

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Original languageEnglish
Title of host publicationSAI Computing Conference (SAI), 2016
PublisherIEEE Press
ISBN (Electronic)9781467384605
ISBN (Print)9781467384612
DOIs
StatePublished - 1 Sep 2016
EventIEEE Technically Sponsored SAI Computing Conference 2016 - London, United Kingdom
Duration: 13 Jul 201615 Jul 2016

Conference

ConferenceIEEE Technically Sponsored SAI Computing Conference 2016
CountryUnited Kingdom
CityLondon
Period13/07/1615/07/16

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