Data mining of Students Withdrawal at University of Tehran, Focusing on Fee Paid Students (to prevent customer churn)

Document Type : Research Paper

Authors

1 Prof. Faculty of Management , Payam Noor University West Unit, Tehran, Iran

2 Assistant Prof. Faculty of Computer Engineering , Payam Noor University, Tehran, Iran

3 MSc. Student in Information Technology Management, Faculty of Manegent Payam Noor University of Tehran, Iran

Abstract

Student withdrawal in higher education is one the important challenges in universities. This paper considers the admission of fee paid students as a business and their withdrawals as customer churn. The aim is to investigate the attrition and predicted risk of attrition to adapt interventionist polices deterrent. This study is a descriptive an applicable technique that uses quantitative and qualitative data. It uses Crisp technology of data mining. The data are derived from educational system of University of Tehran including 21420 fee paid students accepted at 2010 to 2014. The main goal is to analyze the behavior that is at risk of attrition and withdrawal. After data analyze and construction of predictive modeling, the probability table of attrition and regression model will be presented. The final results show that the first and second semester (especially the age range 24-31) of M.Sc students are the most likely risk of withdrawal of happening. 

Keywords


Baker, R. & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1 (1): 3-17.
Barker, K., Trafalis, T. & Rhoads, T. (2004). IEEE Systems and Information Engineering Design Symposium, University of Oklahoma.
Beck, J. & Mostow, J. (2008). A Case Study Empirical Comparsion of Three Methods. 9th international conference on Intelligent Tutoring Systems, June 23-27, Montral Canada.
Bienkowski, M., Feng M. & Means B. (2012). Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief, Available in: http://www.cra.org/ccc/files/docs/learning-analytics-ed.pdf.
Bogard, M. (2013). A Data Driven Analytic Strategy for Increasing Yield and Retention at Western Kentucky University Using SAS Enterprise BI and SAS Enterprise Miner. Available in: http://support.sas.com/resources/papers/ proceedings13/044-2013.pdf.
Bogard, M., James, C., Helbig, T. & Huff, G. (2012). Using SAS® Enterprise BI and SAS® Enterprise Miner TM to Reduce Student Attrition. SAS Conference Proceedings: SAS Global Forum 2012, April 22-25, Orlando, Florida.
Burez, J. & Van den Poel, D. (2007). Using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Systems with Applications, 32(2): 277–288.
Buttle, F. (2008). Customer Relationship Management, UK, Routledge.
Campbell, J.P., Oblinger, D.G. (2007). Academic Analytics. Available in: http://net.educase.edu/ir/library/pdf/PUB6101.pdf.
Dekker, G.W., Pechenizkiy, M. & Vleeshouwers J.M. (2009). Predicting student drop out: A case study. 2nd International Educational Data Mining Conference, July 1-3, Cordoba Spain.
Delen, D. (2010). ‌A comparative analysis of machine learning techniques for student retention management. Decision Support Systems,‌ ‌49 (4): ‌498-506.
García, E., Romero, C., Ventura, S. & Castro, C. (2011). A collaborative educational association rule mining tool. The Internet and Higher Education, 14 (2): 77-88.
Hadden, J., Tiwari, A., Roy, R. & Ruta, D. (2007). Computer assisted customer churn management: State-of-the-art and future trends. Computers and Operations Research, 34(10): 2902–2917.
Herzog, S. (2005). Measuring Determinants of Student Return VS. Dropout/ Stopout VS. Transfer: A First-to-Second Year Analysis of New Freshmen. Research in Higher Education, 46(8): 883–928.
Honigman, B. (2013). 5 Secrets to Increasing Customer Retention -- and Profits, Available in: http://www.entrepreneur.com/article/227946
Kabakchieva, D. (2013). Predicting Student Performance by Using Data Mining Methods for Classification. Cybernetics and Information Technologies, 13(1): 61–72.
Kabakchieva, D., Stefanova, K. & Kisimov, V. (2011). Analyzing University Data for Determining Student Profiles and Predicting Performance, 4th International Conference on Educational Data Mining, Eindhoven, July 6-8, The Netherlands.
Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G. & Loumos, V. (2009). Dropout prediction in e-learning courses through the combination of machine learning techniques. Computers & Education, 53(3): 950-965.
Murphy, E.C. & Murphy, M.A. (2013). Lead­ing on the Edge of Chaos, John Wiley & Sons, Canada. Available in: http://www.impactlearning.com/resources /metrics /customer-retention.
Nandeshwar, A. & Chaudhari, S. (2009). Enrollment prediction models using data mining. Avialable in: http://nandeshwar.info/wp-content/uploads/2008/11/ DM WVU_Project.pdf.
Nandeshwar, A., Menzies, T. & Nelson, A. (2011). Learning patterns of university student retention. Expert Systems with Applications, 38(12): 14984–14996.
Náplava, P. & Šnorek, M. (2003). Modeling of Student's Quality by Means of GMDH Algorithms. System Analysis Modeling Simulation, 43(10): 1415-1426.
Pechenizkiy, M., Calders, T., Vasilyeva, E. & Bra, P. D. (2008). Mining the student assessment data: Lessons drawn from a small scale case study. in 1stInternational Educational Data Mining Conference (EDM2008). Montreal Canad, June 20-21 2008.
Pittman, K. (2008). Comparison of data mining techniques used to predict student retention. PhD thesis, Nova Southeastern University, USA.
Romero, C. & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1): 135–146.
Romero, C. (2008). Data mining algorithms to classify students. in 1st International Educational Data Mining Conference (EDM2008), June 20-21 Canada.
Romero, C., Ventura, S., Barnes, T. & Desmarais, M. (2009). Recommendation in higher education using data mining techniques, 2nd International Conference on Educational Data Mining, EDM 2009, July 1-3, Cordoba Spain.
Scott, G., Shah, M., Grebennikov, L. & Singh, H. (2008). Improving student retention: A University of Western Sydney case study. Journal of Institutional Research, 14(1): 9–23.
Sujitparapitaya, S. (2006). Considering student mobility in retention outcomes. New Direction for Institutional Research, 2006(131): 35-51.
Superby, J., Vandamme, J. & Meskens, N. (2006). Determination of factors influencing the achievement of the first-year university students using data mining methods. Workshop on Educational Data Mining at the 8th International Conference on Intelligent Tutoring Systems (ITS 2006), Hong kong, 37-44.
Tantuco N. & Uy R. (2014). Creating Long-term Loyalty Relationships. Available in:http://catalogue.pearsoned.co.uk/assets/hip/gb/hip_gb_pearsonhighered/samplechapter/0273755021.pdf.
Wang, M.C. (2005). Using Data Mining Techniques to Predict Student Development and Retention. in 2005 National Student Affairs Assessment and Retention Conference, June 3, USA.
Yu, C. H., Digangi, S., Jannasch-pennell, A. & Kaprolet, C. (2010). A data mining approach for identifying  predictors of student retention from sophomore to junior year. Journal of Data Science, 8(2): 307–325.
Yu, Ch. H., Digang, S., Jannasch, A., Kaprolet, Ch. (2010). A Data Mining Approach for Identifying Predictors of Student from sophomore to Junior Year. Journal of Data Science, 8 (2): 307-325.
Zhang, Y., Oussena, S., Clark, T. & Kim, H. (2010). Use Data Mining To Improve Student Retention in Higher Education - A Case Study. in 12th International Conference on Enterprise Information Systems (ICEIS), June 8-1, Madeira Portugal.