Investigation of Factors for Student’s Decision in Studying at Phetchabun Rajabhat University by Data Mining Technique


  • Jetsadaporn Pakamwang Faculty of Science and Technology, Phetchabun Rajabhat University
  • Kan Khoomsab Faculty of Science and Technology, Phetchabun Rajabhat University
  • Kriengkri Timsorn Faculty of Science and Technology, Phetchabun Rajabhat University


data mining, artificial neural network, decision tree, attributes selection, data science


This study presents the use of data mining for investigation of important factors that affect students’ decisions for studying at Phetchabun Rajabhat University. 400 students, including 200 freshmen of Phetchabun Rajabhat University and 200 other freshmen from the other universities, were studied based on questionnaires that had 14 attributes. Score values were collected from the attributes and input data were first analyzed for data classification by ANN and decision tree techniques. 10-fold cross validation and RMSE values were also employed to evaluate classification accuracy. Then, factor identification based on filter ranking method was investigated. Its results were explained by decision tree visualization and IF-THEN rules. The results showed that 400 students were correctly classified into two groups (1) freshmen group from Phetchabun Rajabhat University and (2) freshmen group from the other universities. The classification accuracy of ANN and decision tree was 93.00% and 88.25%, respectively. RMSE (root-mean-square deviation) values were 0.2574 and 0.3031, respectively. Based on filter ranking method, the identified factors affecting students’ decisions were ranked as university brand recognition, family income, and number of majors, university location, parents’ careers and modern curriculum, respectively. From these results, data mining techniques with ANN and decision tree are useful for data processing and they provide meaningful information for university admission system analysis as well as other applications.


Download data is not yet available.



Ahmad, M.W., Mourshed, M., & Rezgui, Y. (2017). Trees vs neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption. Energy and Buildings, 147(1), 77-89.

Ahmed, A.M., Rizaner, A., & Ulusoy, A.H. (2016). Using data mining to predict instructor performance. Procedia Computer Science, 102(1), 137-142.

Angeli, C., Howard, S.K., Ma, J., & Yang, J. (2017). Data mining in educational technology classroom research: Can it make a contribution? Computer & Education, 113(1), 226-242.

Deng, W.J., Chen, W.C., & Pei, W. (2008). Back-propagation neural network based importance-performance analysis for determining critical service attributes. Expert Systems with Applications, 34(2), 1115-1125.

Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques. San Francisco: Morgan Kaufmann Publishers

Hsu, C.H. (2009). Data mining improve industrial standards and enhance production and marketing: An empirical study in apparel industry. Expert Systems with Applications, 36(3), 4185-4191.

Ippoodom, T. (2017). Thai university crisis when the educational institutions wage a war for students to survive. Retrieved from

Jones, D.E., Ghandehari, H., & Facelli, J.C. (2016). A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles. Computer Methods and Programs in Biomedicine, 132(1), 93-103.

Jothi, N., Rashid, N.A.A., & Husain, W. (2015). Data mining in Healthcare–A Review. Procedia Computer Science, 72(1), 306-313.

Kaur, P., Singh, M., & Josan, G.S. (2015). Classification and prediction based data mining algorithms to predict slow learners in education sector. Procedia Computer Science, 57(1), 500-508.

Liao, S.H., Chu, P.H., & Hsiao, P.Y. (2012). Data mining techniques and applications: A-decade review from 2000 to 2011. Expert Systems with Applications, 39(12), 11303-11311.

Lou, W., & Nakai, S. (2001). Application of artificial neural networks for predicting the thermal inactivation of bacteria: A combined effect of temperature, pH and water activity. Food Research International, 34(7), 573-579.

Lui, X., Li, Q., Li, T., & Chen, D. (2018). Differentially private classification with decision tree ensemble. Applied Soft Computing, 62, 807-816.

Natek, S., & Zwilling, M. (2014). Student data mining solution-knowledge management system related to Higher Education Institutions. Expert Systems with Applications, 41(14), 6400-6407.

Ozyirmidokuz, E.K., Uyar, K., & Ozyirmidokuz, M.H. (2015). A data mining based approach to a firm’s marketing channel. Procedia Economics and Finance, 27, 77-84.

Packianather, M.S., Davies, A., Harraden, S., Soman, S., & White, J. (2017). Data mining techniques applied to a manufacturing SME. Procedia CIRP, 62(1), 123-128.

Panto, O., & Theantong, M. (2014). A comparative efficiency of classification of VARK learning style using data mining techniques. Journal of Industrial Technology Ubon Ratchathani Rajabhat University, 4(1), 1-11. (in Thai)

Phang, S.L. (2012). Factors influencing international students’ study destination decision abroad. Master of Communication Thesis, University of Gothenburg.

Pu, Y., Apel, D.B., & Lingga, B. (2018). Rockburst prediction in kimberlite using decision tree with incomplete data. Journal of Sustainable Mining, 17(3), 158-165.

Rodrigues, M.W., Isotani, S., & Zarate, L.E. (2018). Educational data mining: A review of evaluation process in the e-learning. Telematics and Informatics, 35(6), 1701-1717.

Schmitz, GJ., Aldrich, C., & Gouws, F.S. (1999). ANN-DT: An algorithm for extraction of decision trees from artificial neural networks. IEEE Transactions on Neural Networks, 10(6), 1392-401.

Sen, B., & Ucar, E. (2012). Evaluating the achievements of computer engineering department of distance education students with data mining methods. Procedia Technology, 1(1), 262-267.

Sen, B., Ucar, E., & Delen, D. (2012). Predicting and analyzing secondary education placement-test scores: A data mining approach. Expert Systems with Applications, 39(10), 9468-9476.

Sittichat, S. (2017). Study of educational attributes using data mining technique. Information Technology Journal, 13(2), 20-28. (in Thai)

Ramaswami, M., & Bhaskaran, R. (2009). A study on feature selection techniques in educational data mining. Journal of Computing, 1(1), 7-11.

Timsorn, K., Lorjaroenphon, Y., & Wongchoosuk, C. (2017). Identification of adulteration in uncooked Jasmine Rice by a portable low-cost artificial olfactory system. Measurement, 108(1), 67-76.

Timsorn, K., Thoopboochagorn, T., Lertwattanasakul, N., & Wongchoosuk, C. (2016). Evaluation of bacterial population on chicken meats using a briefcase electronic nose. Biosystems Engineering, 151(1), 116-125.

Tsai, Y.C., Trang, L.T., & Kobori, K. (2017). Factors influencing international students to study at universities in Taiwan. International Journal for Innovation Education and Research, 5(1), 1-11.