Use data mining to improve student retention in HE - a case study

Zhang, Ying, Oussena, Samia, Clark, Tony and Kim, Hyeonsook (2010) Use data mining to improve student retention in HE - a case study. In: 12th International Conference on Enterprise Information Systems (ICEIS 2010), 8-12 Jun 2010, Funchal, Portugal.

[img]
Preview
PDF
[Cam_Ready]ICEIS2010 Use Data Mining_Ying.pdf - Accepted Version

Download (1MB) | Preview

Abstract

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)
Uncontrolled Keywords: Data Mining, Higher Education, Student Retention, Student Intervention
Subjects: Computing
Depositing User: Vani Aul
Date Deposited: 21 Feb 2014 16:18
Last Modified: 27 Jul 2017 10:25
URI: http://repository.uwl.ac.uk/id/eprint/723

Downloads

Downloads per month over past year

Actions (login required)

View Item View Item

Menu