Zhang, Ying, Oussena, Samia, Clark, Tony and Kim, Hyeonsook (2010) Use data mining to improve student retention in HE - a case study. In: ICEIS 2010, 12th International Conference on Enterprise Information Systems, 08-12 June 2010, Funchal, Madeira - Portugal.Full text not available from this repository.
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)|
|Subjects:||Computer science, knowledge and information systems|
|Depositing User:||Vani Aul|
|Date Deposited:||21 Feb 2014 16:18|
|Last Modified:||09 Aug 2016 15:01|
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