Using data mining to improve student retention in HE: a case study.

Zhang, Ying, Oussena, Samia, 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. . [Conference or Workshop Item]

Preview

PDF - Final accepted version (with author's formatting) 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.

Actions (login required)

View Item

Statistics

DownloadsShow export options

Export as CSVXMLJSON

Activity Overview

6 month trend

2,452Downloads

6 month trend

2,284Hits

Additional statistics are available via IRStats2.