Authors:
Ying Zhang
1
;
Samia Oussena
1
;
Tony Clark
2
and
Hyeonsook Kim
2
Affiliations:
1
Thames Valley University, United Kingdom
;
2
Middlesex University, United Kingdom
Keyword(s):
Data Mining, Higher Education, Student Retention, Student Intervention.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Data Warehouses and OLAP
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Management
;
Ontologies and the Semantic Web
;
Organisational Issues on Systems Integration
;
Sensor Networks
;
Signal Processing
;
Society, e-Business and e-Government
;
Soft Computing
;
Web Information Systems and Technologies
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.