PREDICTING ACADEMIC ACHIEVEMENT USING DATA MINING METHODS

Yıl 2024, , 443 - 454, 30.06.2024

Mustafa Yağcı

https://doi.org/10.21923/jesd.1380197

Öz

This study proposes a new model to analyze the grade point averages (GPAs) in the previous semester using data mining algorithms and to predict the final GPAs that students may receive in the following semesters in three gradually expanding categories (department, faculty, and university). The performances of the Random Forest, Linear Regression, Support Vector Machines, and k-Nearest Neighbors algorithms, which are among the data mining algorithms, were calculated and compared to estimate the GPAs of the students at the end of the semester. All algorithms applied correctly classified the samples at rates varying between 92% and 94%. The proposed model correctly estimated students’ grade point averages at the end of the semester with an average deviation of 0.28 points over a 4 with a single variable. Students with a high risk of failure can be determined in advance by estimating their final grade point averages.

Anahtar Kelimeler

Data Mining, Machine Learning, Predict Of Academic Performance, Educational Data Mining, Early Warning Systems

AKADEMİK BAŞARININ VERİ MADENCİLİĞİ YÖNTEMLERİYLE TAHMİN EDİLMESİ

Öz

Bu çalışmada, öğrencilerin bir önceki döneme ait dönem sonu not ortalamalarını veri madenciliği yöntemleri ile analiz ederek sonraki dönemlerde alabileceği dönem sonu not ortalamalarını giderek genişleyen 3 kategoride (Bölüm, Fakülte, Üniversite bazında) tahmin edecek yeni bir model önerilmiştir. Veri seti, Türkiye’de bir Devlet Üniversitesindeki tüm öğrenci kayıtlarının tutulduğu Öğrenci Bilgi Sisteminden (ÖBS) alınmıştır. Veriler, Sınıf öğretmenliği bölümünden 426, Eğitim fakültesinden 2.379 ve Üniversite genelinde eğitim gören 5.149 öğrencinin 2017-2018 Güz ve Bahar Yarıyılı dönem sonu not ortalamalarını içermektedir. Öğrencilerin dönem sonundaki genel not ortalamalarını tahmin etmek için veri madenciliği algoritmalarından rastgele orman, lineer regresyon, destek vektör makineleri ve k-en yakın komşular algoritmalarının başarımı hesaplanmış ve karşılaştırılmıştır. Uygulanan tüm algoritmalar örnekleri %92 ile %94 arasında değişen oranlarda doğru bir şekilde sınıflandırmıştır. Önerilen model, öğrencilerin dönem sonu not ortalamalarını tek bir değişken ile 4 üzerinden 0,28 puanlık ortalama sapma ile doğru tahmin etmiştir. Dönem sonu not ortalamalarının tahmin edilmesi sayesinde başarısız olma riski yüksek olan öğrenciler önceden belirlenebilir.

Anahtar Kelimeler

Veri madenciliği, Makine öğrenmesi, Eğitsel Veri Madenciliği, Akademik performansın tahmin edilmesi, Erken Uyarı Sistemleri

Kaynakça

• Ahmad, Z., & Shahzadi, E. (2018). Prediction of students’ academic performance using artificial neural network. Bulletin of Education and Research, 40(3), 157–164.
• Akçapınar, G., Altun, A., & Aşkar, P. (2019). Using learning analytics to develop early-warning system for at-risk students. International Journal of Educational Technology in Higher Education, 16. https://doi.org/10.1186/s41239-019-0172-z
• Aydemir, B. (2017). Veri madenciliği yöntemleri kullanarak meslek yüksekokulu öğrencilerinin akademik başarı tahmini [Predicting academic success of vocational high school students using data mining methods] [Master’s Thesis]. Pamukkale University, Denizli, Turkey. http://hdl.handle.net/11499/2464
• Baker, R. S. J. d., & Yacef, K. (2009). The state of educational data mining in 2009 : A review and future visions. Journal of Educational Data Mining, 1(1), 3-16. https://doi.org/10.5281/zenodo.3554657
• Bernacki, M. L., Chavez, M. M., & Uesbeck, P. M. (2020). Predicting achievement and providing support before STEM majors begin to fail. Computers & Education, 158. https://doi.org/10.1016/j.compedu.2020.103999
• Botchkarev, A. (2018). Performance metrics (error measures) in machine learning regression, forecasting and prognostics: Properties and typology. Retrieved from http://www.gsrc.ca/metrics_typology2018.pdf at 15 February 2021.
• Botchkarev, A. (2019). A new typology design of performance metrics to measure errors in machine learning regression algorithms. Interdisciplinary Journal of Information, Knowledge & Management, 14.
• 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
• Büyüköztürk, Ş. (2008). Sosyal bilimler için veri analizi el kitabı. Ankara: PegemA Yayıncılık (9th ed., p. 201). Ankara: PegemA.
• Calvet Liñán, L., & Juan Pérez, Á. A. (2015). Educational data mining and learning analytics: Differences, similarities, and time evolution. RUSC. Universities and Knowledge Society Journal, 12(3), 98–112. https://doi.org/10.7238/rusc.v12i3.2515
• 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. Proceedings of 2nd IEEE International Conference on Engineering and Technology, ICETECH 2016, March, 431–436. https://doi.org/10.1109/ICETECH.2016.7569290
• Cihan, P., Gökçe, E., & Kalipsiz, O. (2017). Veteriner hekimlik alanında makine öğrenmesi uygulamaları üzerine bir derleme. Kafkas Universitesi Veteriner Fakultesi Dergisi, 23(4), 673–680. https://doi.org/10.9775/kvfd.2016.17281
• Cortes, C., & Vapnik, V. (1995). Supoort-Vector Networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1109/64.163674 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. https://doi.org/10.1007/s10639-020-10316-y
• Cover, T. M., & Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. https://doi.org/10.1007/978-0-387-35973-1_862
• 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, 6(6). 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., & Erven, G. Van. (2019). Educational data mining : Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research, 94, 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). Makine öğrenmesi̇ yöntemleri̇ ile akademi̇k başarının tahmin edilmesi. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 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
• 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
• Hu, Y.-H., Lo, C.-L., & Shih, S.-P. (2014). Developing early warning systems to predict students’ online learning performance. Computers in Human Behavior, 36, 469–478. https://doi.org/10.1016/j.chb.2014.04.002
• Hung, H.-C., Liu, I.-F., Liang, C.-T., & Su, Y.-S. (2020). Applying educational data mining to explore students’ learning patterns in the flipped learning approach for coding education. Symmetry, 12(2). https://doi.org/10.3390/sym12020213
• Kardaş, K., & Güvenir, A. (2020). Kısa sınavların , ödevlerin ve projelerin dönem sonu sınavına olan etkilerinin farklı makine öğrenmesi teknikleri ile araştırılması. EMO Bilgisayar Dergisi, 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). Üniversite öğrenci başarısı üzerine etki eden faktörlerin veri madenciliği yöntemleri ile incelenmesi [Examining the effects on university student success by data mining techniques] [Master’s Thesis]. Eskişehir Osmangazi University, Turkey. http://hdl.handle.net/11684/1256
• 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
• 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
• 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 Ortiz, E. A., & 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
• Pillay, N. (2020). The impact of genetic programming in education. Genetic Programming and Evolvable Machines, 21, 87-97. https://doi.org/10.1007/s10710-019-09362-4
• Ratra, R., & Gulia, P. (2020). Experimental evaluation of open source data mining tools (WEKA and Orange). International Journal of Engineering Trends and Technology, 68(8), 30-35. https://doi.org/10.14445/22315381/IJETT-V68I8P206S
• Rebai, S., Yahia, F. B., & Essid, H. (2020). A graphically based machine learning approach to predict secondary schools performance in Tunisia. Socio-Economic Planning Sciences, 70. 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, 32–47. https://doi.org/10.1016/j.compedu.2019.04.001
• 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, 578–588. https://doi.org/10.1016/j.chb.2018.07.002
• Sutoyo, E., & Almaarif, A. (2020). Educational data mining for predicting student graduation using the naïve bayes classifier algorithm. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(1), 95-101. https://doi.org/10.29207/resti.v4i1.1502
• 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, 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. https://doi.org/10.1016/j.chb.2019.106189
• Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30(1), 79-82.
• 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, 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). https://doi.org/10.1103/PhysRevPhysEducRes.15.020120