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Eğitsel Veri Madenciliği: Adayların Beden Eğitimi ve Spor Eğitimi Programına Yerleşme Durumlarının Tahmini

Yıl 2022, Cilt: 4 Sayı: 1, 110 - 127, 29.06.2022

Mustafa Yağcı Yusuf Ziya Olpak Kağan Gül Sıdıka Seda Olpak

https://doi.org/10.53694/bited.1118025

Öz

Eğitsel veri madenciliğinin temel amacı, eğitimle ilgili konularda karar vermeyi desteklemek için eğitim verilerinden faydalı bilgiler çıkarmaktır. Eğitsel veri madenciliğinde en çok tercih edilen yöntemlerden biri de tahmindir. Mevcut çalışmanın birincil amacı, adayların Beden Eğitimi ve Spor Eğitimi programına kabul edilip edilmeyeceklerini farklı algoritmalar kullanarak tahmin etmektir. Bu araştırma kapsamında 2016-2020 yılları arasında Türkiye'de bir devlet üniversitesinin Beden Eğitimi ve Spor Eğitimi programına katılmak için başvuran 1.671 adaydan elde edilen verilerle çalışılmıştır. Random Forest, kNN, SVM, Logistic Regression ve Naïve Bayes algoritmalarının her biri, bir adayın ilgili programına kabul edilip edilmeyeceğini tahmin etmek için kullanılmıştır. Elde edilen bulgulara göre algoritmaların sınıflandırma doğruluğu en yüksekten en düşüğe doğru sırasıyla; Random Forest (.985), SVM (.845), kNN (.818), Naïve Bayes (.815) ve Logistic Regression (.701) şeklindedir. Başka bir deyişle, Random Forest algoritmasının, örnekleri neredeyse tam olarak doğru bir şekilde sınıflandırdığı bulunmuştur. Bu bağlamda, çalışmadan elde edilen diğer bulgular ayrıntılı olarak tartışılmış ve gelecek araştırmalar için önerilerde bulunulmuştur.

Anahtar Kelimeler

eğitsel veri madenciliği büyük veri özel yetenek sınavı

Destekleyen Kurum

Yok

Proje Numarası

Yok

Teşekkür

Yok

Kaynakça

  • Abut, F., Yüksel, M. C., Akay, M. F., & Daneshvar, S. (2018). Predicting student’s pass/fail status in an academic course using deep learning : A case study. International Journal of Scientific Research in Information Systems and Engineering, 4(1), 87–91.
  • Acikkar, M., & Akay, M. F. (2009). Support vector machines for predicting the admission decision of a candidate to the School of Physical Education and Sports at Cukurova University. Expert Systems with Applications, 36, 7228–7233. https://doi.org/10.1016/j.eswa.2008.09.007
  • Agrawal, R., & Prabakaran, S. (2020). Big data in digital healthcare: lessons learnt and recommendations for general practice. Heredity, 124(4), 525–534. https://doi.org/10.1038/s41437-020-0303-2
  • 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, Article 40. https://doi.org/10.1186/s41239-019-0172-z
  • Aouifi, H. E., Hajji, M. E., Es-Saady, Y., & Douzi, H. (2021). Predicting learner’s performance through video sequences viewing behavior analysis using educational data-mining. Educational and Information Technologies, 26, 5799–5814. https://doi.org/10.1007/s10639-021-10512-4
  • Asri, H., Mousannif, H., Al Moatassime, H., & Noel, T. (2016). Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Computer Science, 83, 1064-1069. https://doi.org/10.1016/j.procs.2016.04.224
  • Baker, R. S. J. D. (2011). Data mining for education. In B. McGaw, P. Peterson, & E. Baker (Eds.), International Encyclopedia of Education (3rd ed., Vol. 7, pp. 112-118.). Elsevier.
  • 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
  • Bakhshinategh, B., Zaiane, O. R., ElAtia, S., & Ipperciel, D. (2018). Educational data mining applications and tasks: A survey of the last 10 years. Education and Information Technologies, 23, 537–553. https://doi.org/10.1007/s10639-017-9616-z
  • Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. A. F., & Nielsen, H. (2000). Assessing the accuracy of prediction algorithms for classification: An overview. Bioinformatics, 16(5), 412–424. https://doi.org/10.1093/bioinformatics/16.5.412
  • Belavagi, M. C., & Muniyal, B. (2016). Performance evaluation of supervised machine learning algorithms for intrusion detection. Procedia Computer Science, 89, 117-123. https://doi.org/10.1016/j.procs.2016.06.016
  • 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
  • Deist, T. M., Dankers, F. J. W. M, Valdes, G., Wijsman, R., Hsu, I.-C., Oberije, C., Lustberg, T., van Soest, J., Hoebers, F., Jochems, A., El Naqa, I., Wee, L., Morin, O., Raleigh, D. R., Bots, W., Kaanders, J. H., Belderbos, J., Kwint, M., Solberg, T.,…Lambin, P. (2018). Machine learning algorithms for outcome prediction in (chemo) radiotherapy: An empirical comparison of classifiers. Medical Physics, 45(7), 3449-3459. https://doi.org/10.1002/mp.12967
  • 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
  • De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. American Institute of Physics, 1644, 97–104. https://doi.org/10.1063/1.4907823
  • Gerber, M. S. (2014). Predicting crime using Twitter and kernel density estimation. Decision Support Systems, 61(1), 115–125. https://doi.org/10.1016/j.dss.2014.02.003
  • Hallikainen, H., Savimäki, E., & Laukkanen, T. (2020). Fostering B2B sales with customer big data analytics. Industrial Marketing Management, 86, 90–98. https://doi.org/10.1016/j.indmarman.2019.12.005
  • Hanna, M. (2004). Data mining in the e-learning domain. Campus-Wide Information Systems, 21(1), 29–34. https://doi.org/10.1108/10650740410512301
  • Hasnain, M., Pasha, M. F., Ghani, I., Imran, M., Alzahrani, M. Y., & Budiarto, R. (2020). Evaluating trust prediction and confusion matrix measures for web services ranking. IEEE Access, 8, 90847–90861. https://doi.org/10.1109/ACCESS.2020.2994222
  • Hernandez-Suarez, A., Sanchez-Perez, G., Toscano-Medina, K., Perez-Meana, H., Portillo-Portillo, J., Sanchez, V., & Villalba, L. J. G. (2019). Using twitter data to monitor natural disaster social dynamics: A recurrent neural network approach with word embeddings and kernel density estimation. Sensors, 19(7), Article 1746. https://doi.org/10.3390/s19071746
  • 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
  • Hussain, S., Atallah, R., Kamsin, A., & Hazarika, J. (2019). Classification, clustering and association rule mining in educational datasets using data mining tools: A case study. In R. Silhavy (Eds.), Cybernetics and Algorithms in Intelligent Systems. CSOC2018. Advances in Intelligent Systems and Computing (Vol. 765, pp. 196–211). Springer https://doi.org/10.1007/978-3-319-91192-2_21
  • Imamovic, D., Babovic, E., & Bijedic, N. (2020). Prediction of mortality in patients with cardiovascular disease using data mining methods. In Proceedings, 19th International Symposium INFOTEH-JAHORINA (pp. 1-4). IEEE. https://doi.org/10.1109/INFOTEH48170.2020.9066297
  • Janssens, A. C. J. W., & Martens, F. K. (2020). Reflection on modern methods: Revisiting the area under the ROC Curve. International Journal of Epidemiology, 49(4), 1397–1403. https://doi.org/10.1093/ije/dyz274
  • Kamuk, Y. (2019). Evaluation of the sports faculties’ talent-based selection exams in the light of the new higher education examination system. Spormetre the Journal of Physical Education and Sport Sciences, 17(3), 222–236. https://doi.org/10.33689/spormetre.510632
  • Karlos, S., Kostopoulos, G., & Kotsiantis, S. (2020). Predicting and interpreting students’ grades in distance higher education through a semi-regression method. Applied Sciences, 10(23), 1–19. https://doi.org/10.3390/app10238413
  • Karthikeyan, V. G., Thangaraj, P., & Karthik, S. (2020). Towards developing hybrid educational data mining model (HEDM) for efficient and accurate student performance evaluation. Soft Computing, 24, 18477–18487. https://doi.org/10.1007/s00500-020-05075-4
  • Kizir, E., Temel, C., & Güllü, M. (2014). Examination of methods for student selection to the schools of physical education and sports in Turkey. Spormetre the Journal of Physical Education and Sport Sciences, 12(2), 133–138. https://doi.org/10.1501/Sporm_0000000261
  • Kılıç Depren, S., Aşkın, Ö. E., & Öz, E. (2017). Identifying the classification performances of educational data mining methods: A case study for TIMSS. Educational Sciences: Theory & Practice, 17(5), 1605–1623. https://doi.org/10.12738/estp.2017.5.0634
  • Line, N. D., Dogru, T., El-Manstrly, D., Buoye, A., Malthouse, E., & Kandampully, J. (2020). Control, use and ownership of big data: A reciprocal view of customer big data value in the hospitality and tourism industry. Tourism Management, 80. https://doi.org/10.1016/j.tourman.2020.104106
  • Marzban, C. (2004). The ROC Curve and the area under it as performance measures. Weather and Forecasting, 19(6), 1106–1114. https://doi.org/10.1175/825.1
  • Márquez-Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Fardoun, H. M., & Ventura, S. (2016). Early dropout prediction using data mining: A case study with high school students. Expert Systems, 33(1), 107–124. https://doi.org/10.1111/exsy.12135
  • 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
  • Orange Data Mining. (2021). Orange Widgets. https://orangedatamining.com/widget-catalog/evaluate/testandscore/
  • Ölçme, Seçme ve Yerleştirme Merkezi. (2021). 2020 Yükseköğretim Kurumları Sınavı Sayısal Veriler [Numerical Data for the 2020 Higher Education Institutions Exam]. https://dokuman.osym.gov.tr/pdfdokuman/2020/YKS/yks_sayisal_27072020.pdf
  • Padmavaty, V., Geetha, C., & Priya, N. (2020). Analysis of data mining tool Orange. International Journal of Modern Agriculture, 9(4), 1146–1150. http://www.modern-journals.com/index.php/ijma/article/view/485
  • Pattiasina, T., & Rosiyadi, D. (2020). Comparison of data mining classification algorithm for predicting the performance of high school students. Jurnal Techno Nusa Mandiri, 17(1), 23–30. https://doi.org/10.33480/techno.v17i1.1226
  • Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man and Cybernetics—Part C: Applications and Reviews, 40(6), 601–618. https://doi.org/10.1109/TSMCC.2010.2053532
  • Romero, C., & Ventura, S. (2013). Data mining in education. WIREs Data Mining and Knowledge Discovery, 3, 12–27. https://doi.org/10.1002/widm.1075
  • Saa, A. A. (2016). Educational data mining & students’ performance prediction. International Journal of Advanced Computer Science and Applications, 7(5), 212–220. https://dx.doi.org/10.14569/IJACSA.2016.070531
  • Sokkhey, P., & Okazaki, T. (2020). Developing web-based support systems for predicting poor- performing students using educational data mining techniques. International Journal of Advanced Computer Science and Applications, 11(7), 23–32. https://dx.doi.org/10.14569/IJACSA.2020.0110704
  • Uddin, S., Khan, A., Hossain, M. E., & Moni, M. A. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics & Decision Making, 19, Article 281. https://doi.org/10.1186/s12911-019-1004-8
  • 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
  • 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, Article 106189. https://doi.org/10.1016/j.chb.2019.106189
  • Yulia, L. W. S. (2020). Predicting student performance in higher education using multi-regression models. TELKOMNIKA (Telecommunication, Computing, Electronics and Control), 18(3), 1354–1360. https://doi.org/10.12928/TELKOMNIKA.v18i3.14802

Educational Data Mining: Predicting Candidates’ Placement Status in Physical Education and Sports Education Program

Yıl 2022, Cilt: 4 Sayı: 1, 110 - 127, 29.06.2022

Mustafa Yağcı Yusuf Ziya Olpak Kağan Gül Sıdıka Seda Olpak

https://doi.org/10.53694/bited.1118025

Öz

Educational data mining’s primary purpose being to extract useful information from educational data in order to support decision-making on educational issues. One of the most preferred methods in educational data mining is prediction. The primary purpose of the current study is to predict whether or not candidates will be admitted into the PESE program according to different algorithms. Within the scope of this research, data was obtained from 1,671 candidates who applied to join the PESE program of a state university in Turkey between 2016 and 2020 were studied. The Random Forest, kNN, SVM, Logistic Regression, and Naïve Bayes algorithms were each used to predict whether or not a candidate could admit to the PESE program. According to the findings, the algorithms’ classification accuracy from highest to lowest is Random Forest (.985), SVM (.845), kNN (.818), Naïve Bayes (.815), and Logistic Regression (.701), respectively. In other words, the Random Forest algorithm is shown to have correctly classified the instances almost exactly. Other findings from the study are discussed in detail, and suggestions put forth for future research.

Anahtar Kelimeler

educational data mining big data special talent exam

Proje Numarası

Yok

Kaynakça

  • Abut, F., Yüksel, M. C., Akay, M. F., & Daneshvar, S. (2018). Predicting student’s pass/fail status in an academic course using deep learning : A case study. International Journal of Scientific Research in Information Systems and Engineering, 4(1), 87–91.
  • Acikkar, M., & Akay, M. F. (2009). Support vector machines for predicting the admission decision of a candidate to the School of Physical Education and Sports at Cukurova University. Expert Systems with Applications, 36, 7228–7233. https://doi.org/10.1016/j.eswa.2008.09.007
  • Agrawal, R., & Prabakaran, S. (2020). Big data in digital healthcare: lessons learnt and recommendations for general practice. Heredity, 124(4), 525–534. https://doi.org/10.1038/s41437-020-0303-2
  • 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, Article 40. https://doi.org/10.1186/s41239-019-0172-z
  • Aouifi, H. E., Hajji, M. E., Es-Saady, Y., & Douzi, H. (2021). Predicting learner’s performance through video sequences viewing behavior analysis using educational data-mining. Educational and Information Technologies, 26, 5799–5814. https://doi.org/10.1007/s10639-021-10512-4
  • Asri, H., Mousannif, H., Al Moatassime, H., & Noel, T. (2016). Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Computer Science, 83, 1064-1069. https://doi.org/10.1016/j.procs.2016.04.224
  • Baker, R. S. J. D. (2011). Data mining for education. In B. McGaw, P. Peterson, & E. Baker (Eds.), International Encyclopedia of Education (3rd ed., Vol. 7, pp. 112-118.). Elsevier.
  • 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
  • Bakhshinategh, B., Zaiane, O. R., ElAtia, S., & Ipperciel, D. (2018). Educational data mining applications and tasks: A survey of the last 10 years. Education and Information Technologies, 23, 537–553. https://doi.org/10.1007/s10639-017-9616-z
  • Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. A. F., & Nielsen, H. (2000). Assessing the accuracy of prediction algorithms for classification: An overview. Bioinformatics, 16(5), 412–424. https://doi.org/10.1093/bioinformatics/16.5.412
  • Belavagi, M. C., & Muniyal, B. (2016). Performance evaluation of supervised machine learning algorithms for intrusion detection. Procedia Computer Science, 89, 117-123. https://doi.org/10.1016/j.procs.2016.06.016
  • 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
  • Deist, T. M., Dankers, F. J. W. M, Valdes, G., Wijsman, R., Hsu, I.-C., Oberije, C., Lustberg, T., van Soest, J., Hoebers, F., Jochems, A., El Naqa, I., Wee, L., Morin, O., Raleigh, D. R., Bots, W., Kaanders, J. H., Belderbos, J., Kwint, M., Solberg, T.,…Lambin, P. (2018). Machine learning algorithms for outcome prediction in (chemo) radiotherapy: An empirical comparison of classifiers. Medical Physics, 45(7), 3449-3459. https://doi.org/10.1002/mp.12967
  • 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
  • De Mauro, A., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. American Institute of Physics, 1644, 97–104. https://doi.org/10.1063/1.4907823
  • Gerber, M. S. (2014). Predicting crime using Twitter and kernel density estimation. Decision Support Systems, 61(1), 115–125. https://doi.org/10.1016/j.dss.2014.02.003
  • Hallikainen, H., Savimäki, E., & Laukkanen, T. (2020). Fostering B2B sales with customer big data analytics. Industrial Marketing Management, 86, 90–98. https://doi.org/10.1016/j.indmarman.2019.12.005
  • Hanna, M. (2004). Data mining in the e-learning domain. Campus-Wide Information Systems, 21(1), 29–34. https://doi.org/10.1108/10650740410512301
  • Hasnain, M., Pasha, M. F., Ghani, I., Imran, M., Alzahrani, M. Y., & Budiarto, R. (2020). Evaluating trust prediction and confusion matrix measures for web services ranking. IEEE Access, 8, 90847–90861. https://doi.org/10.1109/ACCESS.2020.2994222
  • Hernandez-Suarez, A., Sanchez-Perez, G., Toscano-Medina, K., Perez-Meana, H., Portillo-Portillo, J., Sanchez, V., & Villalba, L. J. G. (2019). Using twitter data to monitor natural disaster social dynamics: A recurrent neural network approach with word embeddings and kernel density estimation. Sensors, 19(7), Article 1746. https://doi.org/10.3390/s19071746
  • 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
  • Hussain, S., Atallah, R., Kamsin, A., & Hazarika, J. (2019). Classification, clustering and association rule mining in educational datasets using data mining tools: A case study. In R. Silhavy (Eds.), Cybernetics and Algorithms in Intelligent Systems. CSOC2018. Advances in Intelligent Systems and Computing (Vol. 765, pp. 196–211). Springer https://doi.org/10.1007/978-3-319-91192-2_21
  • Imamovic, D., Babovic, E., & Bijedic, N. (2020). Prediction of mortality in patients with cardiovascular disease using data mining methods. In Proceedings, 19th International Symposium INFOTEH-JAHORINA (pp. 1-4). IEEE. https://doi.org/10.1109/INFOTEH48170.2020.9066297
  • Janssens, A. C. J. W., & Martens, F. K. (2020). Reflection on modern methods: Revisiting the area under the ROC Curve. International Journal of Epidemiology, 49(4), 1397–1403. https://doi.org/10.1093/ije/dyz274
  • Kamuk, Y. (2019). Evaluation of the sports faculties’ talent-based selection exams in the light of the new higher education examination system. Spormetre the Journal of Physical Education and Sport Sciences, 17(3), 222–236. https://doi.org/10.33689/spormetre.510632
  • Karlos, S., Kostopoulos, G., & Kotsiantis, S. (2020). Predicting and interpreting students’ grades in distance higher education through a semi-regression method. Applied Sciences, 10(23), 1–19. https://doi.org/10.3390/app10238413
  • Karthikeyan, V. G., Thangaraj, P., & Karthik, S. (2020). Towards developing hybrid educational data mining model (HEDM) for efficient and accurate student performance evaluation. Soft Computing, 24, 18477–18487. https://doi.org/10.1007/s00500-020-05075-4
  • Kizir, E., Temel, C., & Güllü, M. (2014). Examination of methods for student selection to the schools of physical education and sports in Turkey. Spormetre the Journal of Physical Education and Sport Sciences, 12(2), 133–138. https://doi.org/10.1501/Sporm_0000000261
  • Kılıç Depren, S., Aşkın, Ö. E., & Öz, E. (2017). Identifying the classification performances of educational data mining methods: A case study for TIMSS. Educational Sciences: Theory & Practice, 17(5), 1605–1623. https://doi.org/10.12738/estp.2017.5.0634
  • Line, N. D., Dogru, T., El-Manstrly, D., Buoye, A., Malthouse, E., & Kandampully, J. (2020). Control, use and ownership of big data: A reciprocal view of customer big data value in the hospitality and tourism industry. Tourism Management, 80. https://doi.org/10.1016/j.tourman.2020.104106
  • Marzban, C. (2004). The ROC Curve and the area under it as performance measures. Weather and Forecasting, 19(6), 1106–1114. https://doi.org/10.1175/825.1
  • Márquez-Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Fardoun, H. M., & Ventura, S. (2016). Early dropout prediction using data mining: A case study with high school students. Expert Systems, 33(1), 107–124. https://doi.org/10.1111/exsy.12135
  • 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
  • Orange Data Mining. (2021). Orange Widgets. https://orangedatamining.com/widget-catalog/evaluate/testandscore/
  • Ölçme, Seçme ve Yerleştirme Merkezi. (2021). 2020 Yükseköğretim Kurumları Sınavı Sayısal Veriler [Numerical Data for the 2020 Higher Education Institutions Exam]. https://dokuman.osym.gov.tr/pdfdokuman/2020/YKS/yks_sayisal_27072020.pdf
  • Padmavaty, V., Geetha, C., & Priya, N. (2020). Analysis of data mining tool Orange. International Journal of Modern Agriculture, 9(4), 1146–1150. http://www.modern-journals.com/index.php/ijma/article/view/485
  • Pattiasina, T., & Rosiyadi, D. (2020). Comparison of data mining classification algorithm for predicting the performance of high school students. Jurnal Techno Nusa Mandiri, 17(1), 23–30. https://doi.org/10.33480/techno.v17i1.1226
  • Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man and Cybernetics—Part C: Applications and Reviews, 40(6), 601–618. https://doi.org/10.1109/TSMCC.2010.2053532
  • Romero, C., & Ventura, S. (2013). Data mining in education. WIREs Data Mining and Knowledge Discovery, 3, 12–27. https://doi.org/10.1002/widm.1075
  • Saa, A. A. (2016). Educational data mining & students’ performance prediction. International Journal of Advanced Computer Science and Applications, 7(5), 212–220. https://dx.doi.org/10.14569/IJACSA.2016.070531
  • Sokkhey, P., & Okazaki, T. (2020). Developing web-based support systems for predicting poor- performing students using educational data mining techniques. International Journal of Advanced Computer Science and Applications, 11(7), 23–32. https://dx.doi.org/10.14569/IJACSA.2020.0110704
  • Uddin, S., Khan, A., Hossain, M. E., & Moni, M. A. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics & Decision Making, 19, Article 281. https://doi.org/10.1186/s12911-019-1004-8
  • 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
  • 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, Article 106189. https://doi.org/10.1016/j.chb.2019.106189
  • Yulia, L. W. S. (2020). Predicting student performance in higher education using multi-regression models. TELKOMNIKA (Telecommunication, Computing, Electronics and Control), 18(3), 1354–1360. https://doi.org/10.12928/TELKOMNIKA.v18i3.14802
Toplam 45 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı, Alan Eğitimleri
Bölüm Araştırma Makaleleri
Yazarlar

Mustafa Yağcı 0000-0003-2911-3909

Yusuf Ziya Olpak 0000-0001-5092-252X

Kağan Gül 0000-0001-5959-8199

Sıdıka Seda Olpak 0000-0001-7557-8227

Proje Numarası Yok
Yayımlanma Tarihi 29 Haziran 2022
Gönderilme Tarihi 17 Mayıs 2022
Kabul Tarihi 13 Haziran 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 4 Sayı: 1

Kaynak Göster

APA Yağcı, M., Olpak, Y. Z., Gül, K., Olpak, S. S. (2022). Educational Data Mining: Predicting Candidates’ Placement Status in Physical Education and Sports Education Program. Bilgi Ve İletişim Teknolojileri Dergisi, 4(1), 110-127. https://doi.org/10.53694/bited.1118025
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Bilgi ve İletişim Teknolojileri Dergisi (BİTED)

Journal of Information and Communication Technologies