USE DATA MINING TO IMPROVE STUDENT RETENTION IN HIGHER EDUCATION - A Case Study

Ying Zhang, Samia Oussena, Tony Clark, Hyeonsook Kim

2010

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.

References

  1. Committee of Public Accounts, 2001-02. Fifty-eighth Report of Session Improving Student Achievement and Widening Participation in Higher Education in England, HC 588.
  2. Cristianini, N., Shawe-Taylor, J., 2000. An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press.
  3. Gabrilson, S., Fabro, D. D. M., Valduriez, P., 2008. Towards the efficient development of model transformations using model weaving and matching transformations, Software and Systems Modeling 2003. Data Mining with CRCT Scores. Office of information technology, Geogia Department of Education.
  4. Han, J. W., Kamber, M., 2006. Data Mining: Concepts and Techniques, 2nd Edition, The Morgan Kaufmann Series in Data Management Systems, Gray, J. Series Editor, Morgan Kaufmann Publishers.
  5. Harry, Z., 2004. The Optimality of Naive Bayes, FLAIRS2004 conference.
  6. Herzog, S., 2006. Estimating student retention and degreecompletion time: Decision trees and neural networks vis-a-vis regression, New Directions for Institutional Research, p.17-33.
  7. Kim, H., Zhang, Y., Oussena, S., and Clark, T., 2009. A Case Study on Model Driven Data Integration for Data Centric Software Development, In Proceedings of ACM First International Workshop on Data-intensive Software Management and Mining.
  8. Luan, J., 2002. Data mining and knowledge management in higher education - potential applications. In Proceedings of AIR Forum, Toronto, Canada.
  9. Mazon, J. N., Trujillo, J., Serrano, M., Piattini, M., 2005. Applying MDA to the development of data warehouses. DOLAP 2005
  10. Minaei-Bidgoli, B., Kortemeyer, G., Punch,W.F., 2004. Enhancing Online Learning Performance: An Application of Data Mining Methods, In Proceeding of Computers and Advanced Technology in Education.
  11. Oizilbash. H., 2008. TVU System Overview, Thames Valley University.
  12. Oussena, S., 2008. Mining Courses Management Systems, Thames Valley University.
  13. Quinlan, J. R., 1986. Induction of Decision Trees. Machine Learning 1, 1 pp.81-106.
  14. Schönbrunn, K., Hilbert, A., 2006. Data Mining in Higher Education, Studies in Classification, Data Analysis, and Knowledge Organization Advances in Data Proceedings of the 30th Annual Conference of the Gesellschaft für Klassifikation e.V., Berlin.
  15. Seidman, A., 1996. Spring Retention Revisited: RET = E Id + (E + I + C)Iv. College and University, 71(4), 18- 20.
  16. National Audition Office, 2007, Staying the course: the retention of students in higher education.
  17. Spasic, I., Ananiadou, S., McNaught, J., Kumar, A., 2005. Text mining and ontologies in biomedicine: Making sense of raw text. Briefings in Bioinformatics 6(3): 239-251.
  18. Superby, J. F., Vandamme, J. P., Meskens, N., 2006. Determination of factors influencing the achievement of the first-year university students using data mining methods. Workshop on Educational Data Mining.
  19. Tinto, V., 1975. Dropout from Higher Education: A TheoreticalSynthesis of Recent Research, Review of Educational Research vol.45, pp.89-125.
  20. Tinto, V., 2000. Taking student retention seriously: rethinking the first year of college, NACADA Journal, Vol. 19 No. 2, pp. 5-10.
  21. Thomas, L., 2002. Student retention in higher education: the role of institutional habitus , Journal of Education Policy, Vol. 17 No. 4, August, pp. 423-442.
  22. Westphal, C., Blaxton, T., 1998. Data Mining Solutions, John Wiley.
  23. Witten, I. H., Frank, E., 2005. Data Mining: Practical machine learning tools and techniques, 2nd Edition, Morgan Kaufmann, San Francisco.
  24. Yorke, M., Longden, B., 2004. Retention and student success in higher education.Society for Research in Higher Education.
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Paper Citation


in Harvard Style

Zhang Y., Oussena S., Clark T. and Kim H. (2010). USE DATA MINING TO IMPROVE STUDENT RETENTION IN HIGHER EDUCATION - A Case Study . In Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8425-04-1, pages 190-197. DOI: 10.5220/0002894101900197


in Bibtex Style

@conference{iceis10,
author={Ying Zhang and Samia Oussena and Tony Clark and Hyeonsook Kim},
title={USE DATA MINING TO IMPROVE STUDENT RETENTION IN HIGHER EDUCATION - A Case Study},
booktitle={Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2010},
pages={190-197},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002894101900197},
isbn={978-989-8425-04-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - USE DATA MINING TO IMPROVE STUDENT RETENTION IN HIGHER EDUCATION - A Case Study
SN - 978-989-8425-04-1
AU - Zhang Y.
AU - Oussena S.
AU - Clark T.
AU - Kim H.
PY - 2010
SP - 190
EP - 197
DO - 10.5220/0002894101900197