Use data mining to improve student retention in higher education: A case study

ICEIS 2010 - Proceedings of the 12th International Conference on Enterprise Information Systems

Volumn 1 DISI, Issue , 2010, Pages 190-197

  Zhang, Ying ^a   Oussena, Samia ^a   Clark, Tony ^b   Kim, Hyeonsook ^b  

^a UNIVERSITY OF WEST LONDON   (United Kingdom)

^b MIDDLESEX UNIVERSITY   (United Kingdom)

Author keywords

Data mining; Higher education; Student intervention; Student retention

Indexed keywords

ACADEMIC PERFORMANCE; FOLLOW UP; HIGHER EDUCATION; INTERVENTION STRATEGY; MACHINE-LEARNING; NATURAL LANGUAGE PROCESSING; POTENTIAL PROBLEMS; STUDENT RETENTION; VISUALIZATION TECHNIQUE;

COMPUTATIONAL LINGUISTICS; DATA MINING; DATA VISUALIZATION; INFORMATION SYSTEMS; LEARNING ALGORITHMS; NATURAL LANGUAGE PROCESSING SYSTEMS; TEACHING; VISUALIZATION;

STUDENTS;

EID: 78649878295     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

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. nd 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 degree- completion 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.