Abstract
This paper presents the study of data mining in the education industry to model the performance for students enrolled in university. Two algorithms of data mining were used. Firstly, a descriptive task based on the K-means algorithm was utilized to select several student clusters. Secondly, a classification task supported two classification techniques, known as decision tree and Naïve Bayes, to predict the dropout because of poor performance in a student’s first four semesters. The student academic data collected during the admission process of those students were used to train and test the models, which were assessed using a cross-validation technique. Experimental results show that the prediction of drop out student is improved, and student performance is monitored when the data from the previous academic enrollment are added.
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References
Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33(1), 135–146 (2007)
Chapman, P.: CRISP-DM 1.0: Step-by-Step Data Mining Guide. SPSS, New York (2000)
Khamis, A., Xu, Y., Mohamed, A.: Comparative study in determining features extraction for islanding detection using data mining techniques: correlation and coefficient analysis. Int. J. Electr. Comput. Eng. (IJECE) 7(3), 1112–1134 (2017)
Arague, F., Roldan, C., Salguero, A.: Factors influencing university drop-out rates. Comput. Educ. 53(3), 563–574 (2009)
Kotsiantis, S., Pierrakeas, C., Pintelas, P.: Preventing student dropout in distance learning systems using machine learning techniques. In: Proceedings of the 7th International Conference Knowledge-Based Intelligent Information and Engineering System (KES), Oxford, pp. 267–274 (2003)
Kuna, H., Garcia-Martinez, R., Villatoro, F.: Pattern discovery in university students desertion based on data mining. In: Proceedings of the IV Meeting on Dynamics of Social and Economic Systems, Buenos Aires, pp. 275–285 (2009)
Kovacic, Z.: Predicting student success by mining enrolment data. Res. Higher Educ. J. 15, 1–20 (2012)
Yadav, S., Bharadwaj, B., Pal, S.: Mining educational data to predict student’s retention: a comparative study. Int. J. Comput. Sci. Inf. Secur. (IJCSIS) 10(2), 113–117 (2012)
Quadril, M., Kalyankar, N.: Drop out feature of student data for academic performance using decision tree techniques. Global J. Comput. Sci. Technol. 10(2), 2–5 (2010)
Zhang, Y., Oussena, S., Clark, T., Kim, H.: Use data mining to improve student retention in higher education—a case study. In: Proceedings of the 12th International Conference on Enterprise Information Systems, pp. 190–197 (2010)
Santoso, L., Yulia.: Predicting student performance using data mining. In: Proceedings of the 5th International Conference on Communication and Computer Engineering (ICOCOE) (2018)
Rao, M., Gurram, D., Vadde, S., Tallam, S., Chand, N., Kiran, L.: A predictive model for mining opinions of an educational database using neural networks. Int. J. Electr. Comput. Eng. (IJECE) 5(5), 1158–1163 (2015)
Santoso, L., Yulia.: Data warehouse with big data technology for higher education, Proc. Comput. Sci. 124(1), 93–99 (2017)
Santoso, L., Yulia.: Analysis of the impact of information technology investments—a survey of Indonesian universities. ARPN JEAS 9(12), 2404–2410 (2014)
Al-Radaideh, Q., Al-Shawakfa, E., Al-Najjar, M.: Mining student data using decision trees. In: Proceedings of the 2006 International Conference on Information Technology (ACIT), pp. 1–5 (2006)
Yudkselturk, E., Ozekes, S., Turel, Y.: Predicting dropout student: an application of data mining methods in an online education program. Eur. J. Open Distance E-Learning 17(1), 118–133 (2014)
Pal, S.: Mining educational data to reduce dropout rates of engineering students. Int. J. Inf. Eng. Electron. Bus. 4(2), 1–7 (2012)
Bhise, R., Thorat, S., Superkar, A.: Importance of data mining in higher education system. IOSR J. Humanit. Soc. Sci. 6(6), 18–21 (2013)
Erdogan, S., Timor, M.: A data mining application in a student database. J. Aeronaut. Space Technol. 2(2), 53–57 (2005)
Bhardwaj, A., Bhardwadj, A.: Modified K-means clustering algorithm for data mining in education domain. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(11), 1283–1286 (2013)
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Santoso, L.W., Yulia (2019). The Analysis of Student Performance Using Data Mining. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi, M. (eds) Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 924. Springer, Singapore. https://doi.org/10.1007/978-981-13-6861-5_48
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DOI: https://doi.org/10.1007/978-981-13-6861-5_48
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