STUDENT DROP OUT FACTOR ANALYSIS AND TREND PREDICTION USING DECISION TREE
Keywords:
Data mining, Classification, Prediction, Student drop outAbstract
Issues relating to increases in student drop-out rates are becoming a top priority in many educational institutions. This paper aims to identify and explore the factors influencing this growing phenomenon focusing on a university in provincial Thailand. Research conducted between 2010 and 2014 targeted Management Science students attending Sakon Nakhon Rajabhat University. Survey database on 14 attributes of 4,163 current students. Data analysis was undertaken using algorithm J48 Data Mining techniques with a decision- tree classification and Weka's 10-fold cross validation program. The findings of the research indicated that the four most significant factors that induced student drop-out were low GPA results, studying loans, earlier educational attainment, and parents' monthly incomes. Further analysis indicated that in the 2010-2011 year low GPA attainment was the most significant factor, and added with studying loans in 2012 to 2013 then plused parents' incomes in 2014. This suggests a trend in line with the Classification Rule that may predict drop-out rates in the current year 2015.
References
Bouckaert, R., Frank, E., Hall, M., Kirkby, R., Reutemann, P., Seewald, A., and Scuse, D. (2013). WEKA Manual for Version 3-7-8, University of Waikato, Hamilton, New Zealand, 327p.
Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., and Wirth, R. (2000). CRISP-DM 1.0 Step-by-step data mining guide, Technical report, SPSS inc, USA, 78p.
Gulati, H. (2015). IEEE Conference Publications; March 11-13, 2015; New Delhi, India, p. 713-716.
Han, J., Kamber, M., and Pei, J. (2011). Data Mining Concepts and Techniques. 3rd ed. Elsevier, USA, 703p.
Hastie, T., Tibshirani, R., and Friedman, J. (2008). The Elements of Statistical Learning Data Mining, Inference, and Prediction. 2nd ed. Springer, 739p. Mohammed, J., and Wagner Meira, JR. (2014). Data Mining and Analysis Fundamental Concepts and Algorithms. Cambridge University Press, NY, USA, 593p.
Omkar, S., and Parag, M. (2015). Predicting Dropout Students Using Data-Mining Techniques. IJR J., 2(1):365-375.
Pahannarat, N., Wangrangsimakul, K., and Pipatpen M. (2009). The Problems of Withdrawal of the Youths Receiving Scholarship from World Vision Foundation of Thailand in Songkhla Province. PNU J., 1(3):130-144.
Thinsungnoen, T., Kulnawin, K., and Thinsungnoen, M. (2012). Payap University Research Symposium 2012; February 17, 2012; Chiang Mai, Thailand, p. 34-42.
Yukselturk, E., Ozekes, S., and Türel, Y. (2014). Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program. EURODL J., 17(1):118-133.
