USE DATA MINING TO IMPROVE STUDENT RETENTION IN HIGHER EDUCATION - A Case Study
Year: 2010
Type: conference-paper
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.
Authors: Ying Zhang, Samia Oussena, Tony Clark, Hyeonsook Kim
Institutions: University of West London, Middlesex University London
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W141358816