Educational data mining: prediction of students' academic performance using machine learning algorithms

Ahmad, Z., & Shahzadi, E. (2018). Prediction of students’ academic performance using artificial neural network. Bulletin of Education and Research, 40(3), 157–164. Alshanqiti, A., & Namoun, A. (2020). Predicting student performance and its influential factors using hybrid regression and multi-label classification. IEEE Access, 8, 203827–203844. https://doi.org/10.1109/access.2020.3036572 Arias Ortiz, E., & Dehon, C. (2013). Roads to success in the Belgian French Community’s higher education system: predictors of dropout and degree completion at the Université Libre de Bruxelles. Research in Higher Education, 54(6), 693–723. https://doi.org/10.1007/s11162-013-9290-y Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers and Education, 113, 177–194. https://doi.org/10.1016/j.compedu.2017.05.007 Aydemir, B. (2017). Predicting academic success of vocational high school students using data mining methods graduate. [Unpublished master’s thesis]. Pamukkale University Institute of Science. Babić, I. D. (2017). Machine learning methods in predicting the student academic motivation. Croatian Operational Research Review, 8(2), 443–461. https://doi.org/10.17535/crorr.2017.0028 Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. Learning analytics (pp. 61–75). Springer. Baker, R. S., & 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. Bernacki, M. L., Chavez, M. M., & Uesbeck, P. M. (2020). Predicting achievement and providing support before STEM majors begin to fail. Computers & Education, 158(August), 103999. https://doi.org/10.1016/j.compedu.2020.103999 Burgos, C., Campanario, M. L., De, D., Lara, J. A., Lizcano, D., & Martínez, M. A. (2018). Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout. Computers and Electrical Engineering, 66(2018), 541–556. https://doi.org/10.1016/j.compeleceng.2017.03.005 Capuano, N., & Toti, D. (2019). Experimentation of a smart learning system for law based on knowledge discovery and cognitive computing. Computers in Human Behavior, 92, 459–467. https://doi.org/10.1016/j.chb.2018.03.034 Casquero, O., Ovelar, R., Romo, J., Benito, M., & Alberdi, M. (2016). Students’ personal networks in virtual and personal learning environments: A case study in higher education using learning analytics approach. Interactive Learning Environments, 24(1), 49–67. https://doi.org/10.1080/10494820.2013.817441 Chakraborty, B., Chakma, K., & Mukherjee, A. (2016). A density-based clustering algorithm and experiments on student dataset with noises using Rough set theory. In Proceedings of 2nd IEEE international conference on engineering and technology, ICETECH 2016, March (pp. 431–436). https://doi.org/10.1109/ICETECH.2016.7569290 Costa-Mendes, R., Oliveira, T., Castelli, M., & Cruz-Jesus, F. (2020). A machine learning approximation of the 2015 Portuguese high school student grades: A hybrid approach. Education and Information Technologies, 26, 1527–1547. https://doi.org/10.1007/s10639-020-10316-y Cruz-Jesus, F., Castelli, M., Oliveira, T., Mendes, R., Nunes, C., Sa-Velho, M., & Rosa-Louro, A. (2020). Using artificial intelligence methods to assess academic achievement in public high schools of a European Union country. Heliyon. https://doi.org/10.1016/j.heliyon.2020.e04081 Delen, D. (2010). A comparative analysis of machine learning techniques for student retention management. Decision Support Systems, 49(4), 498–506. https://doi.org/10.1016/j.dss.2010.06.003 Delen, D. (2011). Predicting student attrition with data mining methods. Journal of College Student Retention: Research, Theory and Practice, 13(1), 17–35. https://doi.org/10.2190/CS.13.1.b Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Van Erven, G. (2019). Educational data mining : Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research, 94(February 2018), 335–343. https://doi.org/10.1016/j.jbusres.2018.02.012 Fidalgo-Blanco, Á., Sein-Echaluce, M. L., García-Peñalvo, F. J., & Conde, M. Á. (2015). Using Learning Analytics to improve teamwork assessment. Computers in Human Behavior, 47, 149–156. https://doi.org/10.1016/j.chb.2014.11.050 García-González, J. D., & Skrita, A. (2019). Predicting academic performance based on students’ family environment: Evidence for Colombia using classification trees. Psychology, Society and Education, 11(3), 299–311. https://doi.org/10.25115/psye.v11i3.2056 Gök, M. (2017). Predicting academic achievement with machine learning methods. Gazi University Journal of Science Part c: Design and Technology, 5(3), 139–148. Hardman, J., Paucar-Caceres, A., & Fielding, A. (2013). Predicting students’ progression in higher education by using the random forest algorithm. Systems Research and Behavioral Science, 30(2), 194–203. https://doi.org/10.1002/sres.2130 Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V.V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, C., & Liao, S.N. (2018). Predicting academic performance: a systematic literature review. In Proceedings companion of the 23rd annual ACM conference on innovation and technology in computer science education (pp. 175–199). Hoffait, A., & Schyns, M. (2017). Early detection of university students with potential difficulties. Decision Support Systems, 101(2017), 1–11. https://doi.org/10.1016/j.dss.2017.05.003 Huang, S., & Fang, N. (2013). Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models. Computers and Education, 61(1), 133–145. https://doi.org/10.1016/j.compedu.2012.08.015 Kardaş, K., & Güvenir, A. (2020). Analysis of the effects of Quizzes, homeworks and projects on final exam with different machine learning techniques. EMO Journal of Scientific, 10(1), 22–29. 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, 500–508. https://doi.org/10.1016/j.procs.2015.07.372 Kılınç, Ç. (2015). Examining the effects on university student success by data mining techniques. [Unpublished master’s thesis]. Eskişehir Osmangazi University Institute of Science. Kotsiantis, S., Tselios, N., Filippidi, A., & Komis, V. (2013). Using learning analytics to identify successful learners in a blended learning course. International Journal of Technology Enhanced Learning, 5(2), 133–150. https://doi.org/10.1504/IJTEL.2013.059088 Lara, J. A., Lizcano, D., Martínez, M. A., Pazos, J., & Riera, T. (2014). A system for knowledge discovery in e-learning environments within the European Higher Education Area—Application to student data from Open University of Madrid, UDIMA. Computers and Education, 72, 23–36. https://doi.org/10.1016/j.compedu.2013.10.009 Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. Educause Review, 46(5), 31–40. Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588–599. https://doi.org/10.1016/j.compedu.2009.09.008 Musso, M. F., Hernández, C. F. R., & Cascallar, E. C. (2020). Predicting key educational outcomes in academic trajectories: A machine-learning approach. Higher Education, 80(5), 875–894. https://doi.org/10.1007/s10734-020-00520-7 Nandeshwar, A., Menzies, T., & Nelson, A. (2011). Learning patterns of university student retention. Expert Systems with Applications, 38(12), 14984–14996. https://doi.org/10.1016/j.eswa.2011.05.048 Ornelas, F., & Ordonez, C. (2017). Predicting student success: A naïve bayesian application to community college data. Technology, Knowledge and Learning, 22(3), 299–315. https://doi.org/10.1007/s10758-017-9334-z Ortiz, E. A., & Dehon, C. (2008). What are the factors of success at University? A case study in Belgium. Cesifo Economic Studies, 54(2), 121–148. https://doi.org/10.1093/cesifo/ifn012 Rebai, S., Ben Yahia, F., & Essid, H. (2020). A graphically based machine learning approach to predict secondary schools performance in Tunisia. Socio-Economic Planning Sciences, 70(August 2018), 100724. https://doi.org/10.1016/j.seps.2019.06.009 Rizvi, S., Rienties, B., & Ahmed, S. (2019). The role of demographics in online learning; A decision tree based approach. Computers & Education, 137(August 2018), 32–47. https://doi.org/10.1016/j.compedu.2019.04.001 Rubin, B., Fernandes, R., Avgerinou, M. D., & Moore, J. (2010). The effect of learning management systems on student and faculty outcomes. The Internet and Higher Education, 13(1–2), 82–83. https://doi.org/10.1016/j.iheduc.2009.10.008 Saqr, M., Fors, U., & Tedre, M. (2017). How learning analytics can early predict under-achieving students in a blended medical education course. Medical Teacher, 39(7), 757–767. https://doi.org/10.1080/0142159X.2017.1309376 Shorfuzzaman, M., Hossain, M. S., Nazir, A., Muhammad, G., & Alamri, A. (2019). Harnessing the power of big data analytics in the cloud to support learning analytics in mobile learning environment. Computers in Human Behavior, 92(February 2017), 578–588. https://doi.org/10.1016/j.chb.2018.07.002 Vandamme, J.-P., Meskens, N., & Superby, J.-F. (2007). Predicting academic performance by data mining methods. Education Economics, 15(4), 405–419. https://doi.org/10.1080/09645290701409939 Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89(July), 98–110. https://doi.org/10.1016/j.chb.2018.07.027 Waheed, H., Hassan, S. U., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R. (2020). Predicting academic performance of students from VLE big data using deep learning models. Computers in Human Behavior, 104(October 2019), 106189. https://doi.org/10.1016/j.chb.2019.106189 Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining practical machine learning tools and techniques (3rd ed.). Morgan Kaufmann. Xing, W., Guo, R., Petakovic, E., & Goggins, S. (2015). Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory. Computers in Human Behavior, 47, 168–181. Xu, X., Wang, J., Peng, H., & Wu, R. (2019). Prediction of academic performance associated with internet usage behaviors using machine learning algorithms. Computers in Human Behavior, 98(January), 166–173. https://doi.org/10.1016/j.chb.2019.04.015 Zabriskie, C., Yang, J., DeVore, S., & Stewart, J. (2019). Using machine learning to predict physics course outcomes. Physical Review Physics Education Research, 15(2), 020120. https://doi.org/10.1103/PhysRevPhysEducRes.15.020120