Estimation of the Academic Performance of Students in Distance Education Using Data Mining Methods

Abstract

Many institutions in the field of education have been involved in distance education with the learning management system. In this context, there has been a rapid increase in data in the e-learning process as a result of the development of technology and the widespread use of the internet. This increase is in the size of large data. Today, big data can be primarily processed, the relationships between data can be discovered, a meaningful conclusion can be drawn, and predictions about the future using big data can be made. However, these data are generally not used in a way to contribute to the people and institutions (educators, education administrators, ministries, etc.) involved in the education process. Therefore, this study aims to estimate the academic success of students who receive education in the distance education process using data mining methods. The reason why data mining is used is that these methods are particularly effective and powerful tools in classification and prediction processes. The methods used in the study are Random Forest, Artificial Neural Networks, Naive Bayes, Support Vector Machines, Logistic Regression, and Deep Learning algorithms, respectively. The dataset includes primary, secondary, and high school students’ data, which were obtained from the learning management system used in the distance education process. As a result, the study findings showed that Deep Learning, Random Forest, and Support Vector Machines algorithms provide prediction success at higher performance than others.

Keywords

• Distance Education
• Academic Performance
• Estimation
• Data Mining
• Artificial Intelligence

References

1. Akcapinar, G., Altun, A., & Aşkar, P. (2015). Modeling students’ academic performance based on their interactions in an online learning environment. Primary education Online, 14(3), 815-824.
2. Akpinar, H. (2000). Information discovery and data mining in databases. Istanbul University Journal of the School of Business, 1-22.
3. Aljarah, I. (2017). Students' academic performance dataset. Kaggle: Your Machine Learning and Data Science Community. https://www.kaggle.com/aljarah/xAPI-Edu-Data
4. Alsuwaiket, M. (2018). Measuring academic performance of students in higher education using data mining techniques [Doctoral dissertation, Loughborough University].
5. Altun, M., Kayikci, K., & Irmak, S. (2019). Estimation of Graduation Grades of Primary Education Students by Using Regression Analysis and Artificial Neural Networks. E-International Journal of Educational Research, 10(3), 29 43. https://doi.org/10.19160/ijer.624839
6. Amrieh, E.A., Hamtini, T., & Aljarah, I. (2016). Mining educational data to predict student’s academic performance using ensemble methods. International Journal of Database Theory and Application, 9(8), 119-136. https://doi.org/10.14257/ijdta.2016.9.8.13
7. Aydemir, E. (2019). Forecasting of the Course Learning Notes by Data Mining Methods. European Journal of Science and Technology, 70 76. https://doi.org/10.31590/ejosat.518899 Aydin, S. (2015). Data Mining and an Application in Anadolu University Open Education System. Journal of Research in Education and Teaching, 4(3), 36-44.
8. Aydogan, I., & Zirhlioglu, G. (2018). Estimation of Student Successes by Artificial Neural Networks. YYU Journal of Education Faculty, 15(1), 577 610. http://dx.doi.org/10.23891/efdyyu.2018.80

9. Ayhan, S., & Erdogmus, S. (2014). Kernel Function Selection for the Solution of Classification Problems via Support Vector Machines. Eskisehir Osmangazi University Journal of Economics and Administrative Sciences, 9(1), 175-201.
10. Bayes, T. (1763). LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, FRS communicated by Mr. Price, in a letter to John Canton, AMFR S. Philosophical transactions of the Royal Society of London, 370-418. https://doi.org/10.1098/rstl.1763.0053
11. Beitel, S. (2005). Applying Artificial Intelligence Data Mining Tools to the Challenges of Program Evaluation. Connecticut.
12. Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing Teaching and Learning through Educational Data Mining and Learning Analytics: An Issue Brief. Office of Educational Technology, US Department of Education.
13. Breiman, L. (2001). Random Forests, Machine Learning, 45(1), 5–32.
14. Bresfelean, V.P., Bresfelean, M., Ghisoiu, N., & Comes, C.A. (2008, June 23-26). Determining students’ academic failure profile founded on data mining methods. Proceedings of the ITI 2008 30th International Conference on Information Technology Interfaces, 317-322, https://doi.org/10.1109/ITI.2008.4588366
15. Butuner, R. (2020). Sentiment Analysis with Deep Learning Methods and Its Use in School Guidance Services, [Master's Thesis, Necmettin Erbakan University]. Coincil of Higher Education Libraries: https://tez.yok.gov.tr/UlusalTezMerkezi/TezGoster?key=fl0Kw4p1rmMDotyKRdYv1BKdBnLg10dCC3PJQ2laOIvx6m-b832uTqLlcfv5bVHP
16. Butuner, R., & Yuksel, H. (2021). Diagnosis and Severity of Depression Disease in Individuals with Artificial Neural Networks Method. International Journal of Intelligent Systems and Applications in Engineering, 9(2), 55-63. https://doi.org/10.18201/ijisae.2021.234
17. Buyrukoglu, S., & Yilmaz, Y., (2021). A Novel Semi-Automated Chatbot Model: Providing Consistent Response of Students’ Email in Higher Education based on Case-Based Reasoning and Latent Semantic Analysis, International Journal of Multidisciplinary Studies and Innovative Technologies, 5(1), 6-12.
18. Calp, M.H. (2019). An estimation of personnel food demand quantity for businesses by using artificial neural networks. Journal of Polytechnic, 22(3), 675-686.
19. Calp, M.H. (2021). Use of Deep Learning Approaches in Cancer Diagnosis. In: Kose U., Alzubi J. (eds) Deep Learning for Cancer Diagnosis. Studies in Computational Intelligence, vol 908. Springer. https://doi.org/10.1007/978-981-15-6321-8_15
20. Calp, M.H., & Kose, U. (2020). Estimation of burned areas in forest fires using artificial neural networks. Ingeniería Solidaria, 16(3), 1-22.
21. Cokluk, O.T.D., & Cirak, G.Y. (2013). The Usage of Artifical Neural Network and Logistic Regresssion Methods in the Classification of Student Achievement in Higher Education. Mediterranean Journal of Humanities, 3(2), 71 79. https://doi.org/10.13114/MJH/201322471
22. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20 (3), 273-297. https://doi.org/10.1007/BF00994018. S2CID 206787478.
23. Cunningham, J. (2017). Predicting student success in a self-paced mathematics MOOC (Order No. 10272808). Available from Pro Quest Dissertations & Theses Global, (1900990574).
24. Dias, S.B., & Dinis, J.A. (2014). Towards an enhanced learning in higher education incorporating distinct learner’s profiles. Educational Technology & Society, 17(1), 307–319.
25. Dogan, A. (2012). Yapay Zeka [Artificial intelligence]. Kariyer Publishing.
26. Dunham, M.H. (2003). Data mining introductory and advanced topics. Prentice Hall.
27. Fix, E., & Hodges, Joseph L. (1951). Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties. USAF School of Aviation Medicine, Randolph Field, Texas.
28. García, E., Romero, C., Ventura, S., & De Castro, C. (2011). A collaborative educational association rule mining tool. The Internet and Higher Education, 14(2), 77-88.
29. Guneri, N., & Apaydin, A. (2004). Logistic Regression Analysis and Neural Networks Approach in the Classification of Students Achievement. Gazi University Journal of Commerce & Tourism Education Faculty, 1, 170-188.
30. Han, J., Pei J. & Kamber, M., (2011). Data Mining: Concepts and Techniques. Elsevier.
31. Ibrahim, Z., & Rusli, D. (September, 2007). Predicting Students’ Academic Performance: Comparıng Artificial Neural Network, Decision Tree and Linear Regression. 21st Annual SAS Malaysia Forum, (s. 5). Shangri-La Hotel, Kuala Lumpur.
32. Algarni, A. (2016). Data mining in education. International Journal of Advanced Computer Science and Applications, 7(6), 456-461.
33. Jung, S., & Huh, J.H. (2019). An Efficient LMS Platform and Its Test Bed. Electronics, 8(2), 154.
34. Karakaya, F., Arik, S., Cimen, O., & Yilmaz, M. (2020). Investigation of the views of biology teachers on distance education during the COVID-19 pandemic. Journal of Education in Science, Environment and Health (JESEH), 6(4), 246-258. https://doi.org/10.21891/jeseh.792984
35. Kilinc, D., Borandag, E., Yucalar, F., Tunali, V., Simsek, M., & Ozcift, A. (2016), Classification of Scientific Articles Using Text Mining with KNN Algorithm and R Language. Marmara Journal of Pure and Applied Sciences, 3, 89-94. https://doi.org/10.7240/mufbed.69674.
36. Kiray, S.A., Gok, B., & Bozkir, A.S. (2015). Identifying the factors affecting science and mathematics achievement using data mining methods. Journal of Education in Science, Environment and Health (JESEH), 1(1), 28-48.
37. Kurt, C., & Erdem, O. (2012). Discovering the Factors Effect Student Success via Data Mining Techniques. Journal of Polytechnic, 15(2), 111-116.
38. Lopez, M.I., Luna, J.M., Romero, C., & Ventura, S. (2012). Classification via clustering for predicting final marks based on student participation in forums. International Educational Data Mining Society.
39. Luan, J. (2002). Data Mining and Knowledge Management in Higher Education-Potential Applications. 42nd Associate of Institutional Research International Conference (s. 1-20). Toronto, Canada: ERIC.
40. Oakes, K. (2002). E-learning: LCMS, LMS—They’re not just acronyms but powerful systems for learning. Training & Development, 56(3), 73–75.
41. Oguzlar, A. (2003). Data Preprocessing. Erciyes University Journal of Faculty of Economics and Administrative Sciences, 21, 67-76.
42. Ozbay, O. (2015). Data Mining Concept and Data Mining Applications in Education. The Journal of International Education Science, 5, 262-272.
43. Ozbay, O., & Ersoy, H. (2017). Analysis of Student Dynamism into Learning Management.
44. Resende, P.A.A., & Drummond, A.C. (2018). A survey of random forest based methods for intrusion detection systems. ACM Computing Surveys (CSUR), 51(3), 1-36.
45. Salman, F.M., Abu-Naser, S.S., Alajrami, E., Abu-Nasser, B.S., & Ashqar, B.A. (2020). COVID-19 Detection using Artificial Intelligence, International Journal of Academic Engineering Research, 18-25.
46. Savas, S., (2021). Artificial Intelligence and Innovative Applications in Education: The Case of Turkey, Journal of Information Systems and Management Research, 3(1), 14-26.
47. Savas, S., Guler, O., Kaya, K., Coban, G., & Guzel, M.S., (2021). Digital Games in Education and Learning through Games, International Journal of Active Learning, 6(2), 117-140.
48. Sembiring, S., Zarlis, M., Hartama, D., Ramliana, S., & Wani, E. (2011). Prediction of Student Academic Performance by an Application of Data Mining Techniques. International Conference on Management and Artificial Intelligence, 6(1), 110-114). IACSIT Press.
49. Sengur, D., & Tekin, A. (2013). Prediction of Student’s Grade Point Average by Using the Data Mining Methods. Journal of Information Technologies, 6(3), 7-16.
50. Siemens, G., & Baker, R. (2012). Prediction of student academic performance by an application of k-means clustering algorithm. Towards Communication and Collaboration. 2nd international conference on learning analytics and knowledge. Vancouver, Canada.
51. Subbanarasimha, P., Arinzeb, B., & Anandarajanb, M. (2000). The Predictive Accuracy of Artificial Neural Networks and Multiple Regression in the Case of Skewed Data. Exploration of Some Issues. Expert Systems with Applications, 117-123.
52. System through Data Mining Methods. Journal of Gazi University, Faculty of Education, 37(2), 523-558.
53. Tolles, J., & Meurer, W.J. (2016). Logistic regression: relating patient characteristics to outcomes. Jama, 316(5), 533-534.
54. Turgut, M., & Baykul, Y. (2013). Eğitimde Ölçme ve Değerlendirme [Measurement and Evaluation in Education]. Pegem Yayıncılık.
55. Turhan, K., Kurt, B., & Engin, Y.Z. (2013). Estimation of Student Success with Artificial Neural Networks. Education and Science, 38(170), 112-120.
56. Uzut, O.G., & Buyrukoglu, S., (2020). Prediction of real estate prices with data mining algorithms, Euroasia Journal of Mathematics, Engineering, Natural and Medical Sciences, 8(9), 77-84.
57. Yamamoto, G. T., & Altun, D. (2020). The Coronavirus and the Rising of Online Education. Journal of University Research, 25-34. https://doi.org/10.32329/uad.711110
58. Yildiz, H.K., Genctav, M., Usta, N., Diri, B., & Amasyalı, M.F. (2007). A New Feature Extraction Method for Text Classification. 2007 IEEE 15th Signal Processing and Communications Applications.
59. Yurtoglu, H. (2005). Yapay Sinir Ağları Modellemesi ile Öngörü Modellemesi: Bazı Makroekonomik Değişkenler için Türkiye Örneği [Predictive Modeling with Artificial Neural Network Modeling: The Case of Turkey for Some Macroeconomic Variables]. [Expertise Thesis, DPT]. https://www.sbb.gov.tr/wp content/uploads/2018/11/HasanYurtoglu.pdf