The Investigation of Student Dropout Prediction Model in Thai Higher Education Using Educational Data Mining: A Case Study of Faculty of Science, Prince of Songkla Uni-versity
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Abstract
The student’s retention rate is one of the challenging issues that representing the quality of the university. A high dropout rate of students affects not only the reputation of the university but also the students’ career in the future. Therefore, there is a need of student dropout analysis in order to improve the academic plan and management to reduce students drop out from the university as well as to enhance the quality of the higher education system. Data mining technique provides powerful methods for analysis and the prediction the dropout. This paper proposes a model for predicting students’ dropout using the dataset from the representative of the largest public university in the Southen part of Thailand. In this study, data from Faculty of Science, Prince of Songkla University was collected from academic year of 2013 to 2017. The experiment result shows that JRip rule induction is the best technique to generate a prediction model receiving the highest accuracy value of 77.30%. The results highlight the potential prediction model that can be used to detect the early state of dropping out of the student which the university can provide supporting program to improve the student retention rate
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J. Gairín, X. M. Triado, M. Feixas, P. Figuera, P. Aparicio-Chueca, and M. Torrado, “Student dropout rates in Catalan universities: profile and motives for disengagement,” Qual. High. Educ., vol. 20, no. 2, pp. 165–182, 2014.
R. Wirth and J. Hipp, “CRISP-DM: Towards a standard process model for data mining,” in Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, 2000, pp. 29–39.
B. Leventhal, “An introduction to data mining and other techniques for advanced analytics,” In Journal of Direct, Data and Digital Marketing Practice, vol. 12, no. 2, pp.137-153, 2010.
J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011.
B. Daniel, “Big Data and analytics in higher education: Opportunities and challenges,” In British journal of educational technology, vol. 46, no. 5 pp. 904-920, 2015.
R. Llorente and M. Morant, “Data mining in higher education,” in New fundamental technologies in data mining, IntechOpen, 2011.
A. M. Shahiri and W. Husain, “A review on predicting student’s performance using data mining techniques,” Procedia Comput. Sci., vol. 72, pp. 414–422, 2015.
C. Romero and S. Ventura, “Educational data mining: a review of the state of the art,” IEEE Trans. Syst. Man, Cybern. Part C (Applications Rev., vol. 40, no. 6, pp. 601–618, 2010.
M. M. N. Quadri and N. V Kalyankar, “Drop out feature of student data for academic performance using decision tree techniques,” Glob. J. Comput. Sci. Technol., 2010.
P. M. Arsad and N. Buniyamin, “A neural network students’ performance prediction model (NNSPPM),” in 2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), 2013, pp. 1–5.
T. Patil, and S. Sherekar, “Performance Analysis of Naive Bayes and J48,” International Journal Of Computer Science And Applications, vol. 6, no. 2, pp. 256-261, 2013.
S. Suthaharan, “Support vector machine,” in Machine learning models and algorithms for big data classification, Springer, 2016, pp. 207–235.
C. Márquez-Vera, C. R. Morales, and S. V. Soto, “Predicting school failure and dropout by using data mining techniques,” In IEEE Journal of Latin-American learning technologies, vol. 8, no. 1, pp. 7-14, 2013.
V. Ramesh, P. Parkavi, and K. Ramar, “Predicting student performance: a statistical and data mining approach,” International Journal of computer applications, vol. 6, no. 8, pp. 35-39, 2013.
A. Tekin, “Early Prediction of Students' Grade Point Averages at Graduation: A Data Mining Approach,” Eurasian Journal of Educational Research, vol. 54, pp. 207-226, 2014.
J. Namdeo, and N. Jayakumar, “Predicting Students Performance Using Data Mining Technique with Rough Set Theory Concepts,” International Journal of Advance Research in Computer Science and Management Studies, vol. 2, no. 2, pp. 367-373, 2014.
M. Mythili and A. Shanavas, “An Analysis of students’ performance using classification algorithms,” Journal of Computer Engineering, vol. 16, no. 1, pp. 63-69, 2014.
F. Ahmad, N. Ismail, and A. A. Aziz, “The prediction of students’ academic performance using classification data mining techniques,” Applied Mathematical Sciences, vol. 9, no. 129, pp. 6415-6426, 2015.
C. E. Guarín, E. León Guzmán, and F. A. González, “A model to predict low academic performance at a specific enrollment using data mining,” IEEE Revista Iberoamericana de tecnologias del Aprendizaje, vol. 10, no. 3, pp. 119-125, 2015.
G. Abu-Oda, and A. El-Halees, "Data Mining in Higher Education: University student dropout case study," International Journal of Data Mining & Knowledge Management Process, vol. 5, no. 1, pp. 15-27, 2015.
L. Aulck, N. Velagapudi, J. Blumenstock, and J. West, "Predicting student dropout in higher education" 2016 ICML Workshop on #Data4Good: Machine learning in Social Good Applications, pp. 16-20, 2016. New York
T. -O. Tran, H. -T. Dang, V. -T. Dinh, and X. -H. Phan, "Performance Prediction for Students: A Multi-Strategy Approach," Cybernetics and Information Technologies, vol. 1, no. 2, pp. 164-182. 2017.
R. T. Pereira, and J. C. Zambrano, J. C. "Application of Decision Trees for Detection of Student Dropout Profiles," In 16th IEEE International Conference on Machine Learning and Applications, 2017, pp. 528-531. IEEE.
K. Watkins, N. Na Phayap, and T. Jirasukprasert, "Intelligent Recommendation Systems using Data Mining techniques for Education Analytics for university," In The 2nd International STEM Education Conference, 2017, pp. 56. Chiang Mai.
N. Iam-On, and T. Boongoen, "Improved student dropout prediction in Thai University using ensemble of mixed-type data clusterings," International Journal of Machine Learning and Cybernetics, vol. 8, no.2, pp. 497-508, 2017.
L. Ramanathan, G. Parthasarathy, K. Vijayakumar, L. Lakshmanan, and S. Ramani, "Cluster-based distributed architecture for prediction of student’s performance in higher education," Cluster Computing The Journal of Networks, Software Tools and Applications, 2018, pp. 1-16.
C. Burgos, M. L. Campanario, D. Pena, J. A. Lara, D. Lizcano, and M. A. Martínez, "Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout," In Computers & Electrical Engineering, vol. 66, pp. 541-556, 2018.
D. Bhanushali, S. Khan, M. Madhia, and S. Majumdar, "Student Performance Prediction And Analysis," International Journal of Advanced Research in Computer and Communication Engineering, vol. 7, no. 3, pp. 255-258, 2018.
U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, "Knowledge Discovery and Data Mining: Towards a Unifying Framework," In KDD, vol. 96, pp. 82-88, 1996.
V. García, A. Marqués, and J. Sánchez, "Non-parametric statistical analysis of machine learning methods for credit scoring," In Advances in Intelligent Systems and Computing, 2012, pp. 263-272. Berlin: Springer.
M. Mayilvaganan, and D. Kalpanadevi, "Comparison of Classification Techniques for predicting the performance of Students Academic Environment," In International Conference on Communication and Network Technologies (ICCNT), 2014, pp. 113-118. IEEE.
S. Kalmegh, "Analysis of WEKA data mining algorithm REPTree, Simple CART and RandomTree for classification of Indian news," International Journal of Innovative Science, Engineering & Technology, vol. 2, no. 2, pp.438-446, 2015.
A. Rajput, R. Prasad Aharwal, M. Dubey, S. Saxena, and M. Raghuvanshi, "J48 and JRIP rules for e-governance data," International Journal of Computer Science and Security (IJCSS), vol. 5, no. 2, pp. 201-207, 2011.
Z. Muda, W. Yassin, M. N. Sulaiman, and N. I. Udzir, “Intrusion detection based on k-means clustering and OneR classification,” in 2011 7th International Conference on Information Assurance and Security (IAS), 2011, pp. 192–197.
S. B. Aher and L. Lobo, “Comparative study of classification algorithms,” Int. J. Inf. Technol., vol. 5, no. 2, pp. 239–243, 2012.
A. Rahman, and S. Dash, "Data Mining for Student's Trends Analysis Using Apriori Algorithm," International Journal of Control Theory and Applications, vol. 10, no. 18, pp. 107-115, 2017.