Using data mining to improve student retention in HE: a case study.
Zhang, Ying and Oussena, Samia and Clark, Tony and Hyensook, Kim (2010) Using data mining to improve student retention in HE: a case study. In: ICEIS - 12th International Conerence on Enterprise Information Systems, 2010., 8-12 June, Portugal.
- Final accepted version (with author's formatting)
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 Areas:||A. > School of Science and Technology > Computer Science
A. > School of Science and Technology > Computer Science > Intelligent Environments Research Group
A. > School of Science and Technology > Computer Science > SensoLab group
|Depositing User:||Tony Clark|
|Date Deposited:||25 May 2010 09:17|
|Last Modified:||29 Sep 2015 10:43|
Actions (login required)