Use Data Mining to Improve Student Retention in Higher Education - A Case Study

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.

[1]  William F. Punch,et al.  Enhancing Online Learning Performance: An Application of Data Mining Methods , 2004, CATE.

[2]  L. Thomas.  Student retention in higher education: the role of institutional habitus , 2002, Bourdieu and Education.

[3]  Maria Morant,et al.  Data Mining in Higher Education , 2011 .

[4]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[5]  R. Barnacle.  Retention and Student Success in Higher Education , 2005 .

[6]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[7]  Mehmed Kantardzic,et al.  Data-Mining Concepts , 2011 .

[8]  Ying Zhang,et al.  A case study on model driven data integration for data centric software development , 2009, CIKM-DSMM.

[9]  Alan Seidman,et al.  Retention Revisited: R=E, Id+E & In, Iv. , 1996 .

[10]  Ian Witten,et al.  Data Mining , 2000 .

[11]  Harry Zhang,et al.  The Optimality of Naive Bayes , 2004, FLAIRS.

[12]  Jing Luan,et al.  Data Mining and Knowledge Management in Higher Education -Potential Applications. , 2002 .

[13]  Nadine Meskens,et al.  Determination of factors influencing the achievement of the first-year university students using data mining methods , 2006 .

[14]  Jian Pei,et al.  Data Mining: Concepts and Techniques, 3rd edition , 2006 .

[15]  Serge Herzog,et al.  Estimating Student Retention and Degree-Completion Time: Decision Trees and Neural Networks Vis-a-Vis Regression. , 2006 .

[16]  Anand Kumar,et al.  Text mining and ontologies in biomedicine: Making sense of raw text , 2005, Briefings Bioinform..

[17]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[18]  Vincent Tinto.  Dropout from Higher Education: A Theoretical Synthesis of Recent Research , 1975 .

[19]  Mario Piattini,et al.  Applying MDA to the development of data warehouses , 2005, DOLAP '05.

[20]  Patrick Valduriez,et al.  Towards the efficient development of model transformations using model weaving and matching transformations , 2009, Software & Systems Modeling.

[21]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .