Research Article
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Data Mining Techniques Based Students Achievements Analysis

Year 2018, Volume: 13 Issue: 2, 53 - 59, 19.09.2018

Abstract

In this work, data mining techniques are used to
determine the students’ achievements in Mathematic class. In other words, we
use the data mining techniques to determine if there is any link between the
student achievement and various student related data such as student grades,
demographic, social information and school related data. Data mining
techniques, Decision Tree (DT), Discriminant Analysis (DA), Support Vector
Machines (SVM), k-nearest neighbor (K-NN) and ensemble learner are used in
prediction purposes. A publicly available dataset is considered in experimental
works. Experimental works, on computer environment are carried out to validate
the data mining techniques. All data mining methodologies are simulated on
MATLAB environment with 5-fold cross-validation technique. The classification
performance is measured by accuracy and root mean square error (RMSE)
criterions. Three experimental setups and for each setup, three scenarios are
considered during experimentation. The obtained results are encouraging and the
comparison with some of the existing achievements shows the superiority of our
work. 

References

  • 1. Cortez, P., and Silva, A. (2008). Using data mining to predict secondary school student performance. In the Proceedings of 5th Annual Future Business Technology Conference, Porto, Portugal, 5-12. 2. Ma, Y., Liu, B., Wong, C., Yu, P., and Lee, S. (2000). Targeting the right students using data mining. In Proc. of 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Boston, USA, 457–464. 3. Minaei-Bidgoli, B., Kashy, D., Kortemeyer, G., and Punch, W. (2003). Predicting student Performance: An application of Data Mining Methods with an educational web-based system. In Proc. of IEEE Frontiers in Education. Colorado, USA, 13–18 4. Şengür, D., and Tekin, A. (2013). Öğrencilerin mezuniyet notlarinin veri madenciliği metotlari ile tahmini. Bilişim Teknolojileri Dergisi, 6(3): 7-16. 5. Kotsiantis S.., Pierrakeas, C., and Pintelas P. (2004). Predicting students’ performance in distance learning using machine learning techniques. Applied Artificial Intelligence (AAI), 18(5): 411–426. 6. Pardos Z., Heffernan N., Anderson B., and Heffernan C. (2006). Using fine-grained skill models to fit student performance with bayesian networks. In Proc. of 8th Int. Conf. on Intelligent Tutoring Systems. Taiwan. 7. Turhan, M., Şengür, D., Karabatak, S., Guo, Y., and Smarandache, F. (2018). Neutrosophic weighted support vector machines for the determination of school administrators who attended an action learning course based on their conflict-handling styles. Symmetry, 10(5): 176. 8. Kabakchieva, D. (2012). Student performance prediction by using data mining classification algorithms. IJCSMR, 1(4): 686-690. 9. Devasia, T., Vinushree, T. P., and Hegde, V. (2016, March). Prediction of students performance using educational data mining. In Data Mining and Advanced Computing (SAPIENCE), International Conference on (pp. 91-95). IEEE. 10. Vyas, M. S., & Gulwani, R. (2017, April). Predicting student's performance using cart approach in data science. In Electronics, Communication and Aerospace Technology (ICECA), 2017 International conference of (Vol. 1, pp. 58-61). IEEE. 11. Safavian, S. R., and Landgrebe, D. (1991). A survey of decision tree classifier methodology, IEEE Transactions on Systems Man and Cybernetics, 21: 660-674. 12. Sengur, A. (2008). An expert system based on linear discriminant analysis and adaptive neuro-fuzzy inference system to diagnosis heart valve diseases. Expert Systems with Applications, 35: 214-222. 13. Hastie T., Tibshirani R., and Friedman J. (2001). “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”. Springer-Verlag, NY, USA. 14. Fix, E., and Hodges, J. L. (1951). Discriminatory Analysis, Nonparametric Discrimination: Consistency Properties; Technique Report No. 4; U.S. Air Force School of Aviation Medicine, Randolf Field Texas: Universal City, TX, USA, pp. 238–247. 15. Sengur, A. (2012). Support vector machine ensembles for intelligent diagnosis of valvular heart disease. Journal of Medical Systems, 36: 2649-2655.
Year 2018, Volume: 13 Issue: 2, 53 - 59, 19.09.2018

Abstract

References

  • 1. Cortez, P., and Silva, A. (2008). Using data mining to predict secondary school student performance. In the Proceedings of 5th Annual Future Business Technology Conference, Porto, Portugal, 5-12. 2. Ma, Y., Liu, B., Wong, C., Yu, P., and Lee, S. (2000). Targeting the right students using data mining. In Proc. of 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Boston, USA, 457–464. 3. Minaei-Bidgoli, B., Kashy, D., Kortemeyer, G., and Punch, W. (2003). Predicting student Performance: An application of Data Mining Methods with an educational web-based system. In Proc. of IEEE Frontiers in Education. Colorado, USA, 13–18 4. Şengür, D., and Tekin, A. (2013). Öğrencilerin mezuniyet notlarinin veri madenciliği metotlari ile tahmini. Bilişim Teknolojileri Dergisi, 6(3): 7-16. 5. Kotsiantis S.., Pierrakeas, C., and Pintelas P. (2004). Predicting students’ performance in distance learning using machine learning techniques. Applied Artificial Intelligence (AAI), 18(5): 411–426. 6. Pardos Z., Heffernan N., Anderson B., and Heffernan C. (2006). Using fine-grained skill models to fit student performance with bayesian networks. In Proc. of 8th Int. Conf. on Intelligent Tutoring Systems. Taiwan. 7. Turhan, M., Şengür, D., Karabatak, S., Guo, Y., and Smarandache, F. (2018). Neutrosophic weighted support vector machines for the determination of school administrators who attended an action learning course based on their conflict-handling styles. Symmetry, 10(5): 176. 8. Kabakchieva, D. (2012). Student performance prediction by using data mining classification algorithms. IJCSMR, 1(4): 686-690. 9. Devasia, T., Vinushree, T. P., and Hegde, V. (2016, March). Prediction of students performance using educational data mining. In Data Mining and Advanced Computing (SAPIENCE), International Conference on (pp. 91-95). IEEE. 10. Vyas, M. S., & Gulwani, R. (2017, April). Predicting student's performance using cart approach in data science. In Electronics, Communication and Aerospace Technology (ICECA), 2017 International conference of (Vol. 1, pp. 58-61). IEEE. 11. Safavian, S. R., and Landgrebe, D. (1991). A survey of decision tree classifier methodology, IEEE Transactions on Systems Man and Cybernetics, 21: 660-674. 12. Sengur, A. (2008). An expert system based on linear discriminant analysis and adaptive neuro-fuzzy inference system to diagnosis heart valve diseases. Expert Systems with Applications, 35: 214-222. 13. Hastie T., Tibshirani R., and Friedman J. (2001). “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”. Springer-Verlag, NY, USA. 14. Fix, E., and Hodges, J. L. (1951). Discriminatory Analysis, Nonparametric Discrimination: Consistency Properties; Technique Report No. 4; U.S. Air Force School of Aviation Medicine, Randolf Field Texas: Universal City, TX, USA, pp. 238–247. 15. Sengur, A. (2012). Support vector machine ensembles for intelligent diagnosis of valvular heart disease. Journal of Medical Systems, 36: 2649-2655.
There are 1 citations in total.

Details

Primary Language English
Journal Section TJST
Authors

Dönüş Şengür

Songül Karabatak This is me

Publication Date September 19, 2018
Submission Date July 13, 2018
Published in Issue Year 2018 Volume: 13 Issue: 2

Cite

APA Şengür, D., & Karabatak, S. (2018). Data Mining Techniques Based Students Achievements Analysis. Turkish Journal of Science and Technology, 13(2), 53-59.
AMA Şengür D, Karabatak S. Data Mining Techniques Based Students Achievements Analysis. TJST. September 2018;13(2):53-59.
Chicago Şengür, Dönüş, and Songül Karabatak. “Data Mining Techniques Based Students Achievements Analysis”. Turkish Journal of Science and Technology 13, no. 2 (September 2018): 53-59.
EndNote Şengür D, Karabatak S (September 1, 2018) Data Mining Techniques Based Students Achievements Analysis. Turkish Journal of Science and Technology 13 2 53–59.
IEEE D. Şengür and S. Karabatak, “Data Mining Techniques Based Students Achievements Analysis”, TJST, vol. 13, no. 2, pp. 53–59, 2018.
ISNAD Şengür, Dönüş - Karabatak, Songül. “Data Mining Techniques Based Students Achievements Analysis”. Turkish Journal of Science and Technology 13/2 (September 2018), 53-59.
JAMA Şengür D, Karabatak S. Data Mining Techniques Based Students Achievements Analysis. TJST. 2018;13:53–59.
MLA Şengür, Dönüş and Songül Karabatak. “Data Mining Techniques Based Students Achievements Analysis”. Turkish Journal of Science and Technology, vol. 13, no. 2, 2018, pp. 53-59.
Vancouver Şengür D, Karabatak S. Data Mining Techniques Based Students Achievements Analysis. TJST. 2018;13(2):53-9.