ZHANG, Ying, OUSSENA, Samia, CLARK, Tony and HYENSOOK, Kim (2010). Using data mining to improve student retention in HE: a case study. In: ICEIS - 12th International Conference on Enterprise Information Systems 2010, Portugal, 8-12 June 2010. (Unpublished)
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Data mining combines machine learning, statistics and visualization techniques to discover and extract knowledge. One of the biggest challenges that higher education faces is to improve student retention (National Audition Office, 2007). Student retention has become an indication of academic performance and enrolment management. Our project uses data mining and natural language processing technologies to monitor student, analyze student academic behaviour and provide a basis for efficient intervention strategies. Our aim is to identify potential problems as early as possible and to follow up with intervention options to enhance student retention. In this paper we discuss how data mining can help spot students ‘at risk’, evaluate the course or module suitability, and tailor the interventions to increase student retention.
|Item Type:||Conference or Workshop Item (Paper)|
|Research Institute, Centre or Group:||Cultural Communication and Computing Research Institute > Communication and Computing Research Centre|
|Depositing User:||Tony Clark|
|Date Deposited:||12 Apr 2016 14:57|
|Last Modified:||19 Oct 2016 20:37|
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