A Study On Profiling Students via Data Mining

Abstract

Data mining is a significant method which is utilized in order to reveal the hidden patterns and connections within big data. The method is used at various fields such as financial transactions, banking, education, health sector, logistics and security. Even though analysis towards the consumption habits of the customers is carried out via association rules mining more often, which is one of the basic methods of data mining, the method is also utilized in order to profile patients and students. As well as the customization of a customer is of high significance, so is distinguishing and customizing a student. Within this study, students were tried to be profiled via data mining of the student data of a high school. A set of qualities, that can directly affect the performance of students such as health conditions, financial resources, life standards and education level of the families, were taken into consideration. For that purpose, upon the analysis of data of 443 students in the database, a data warehouse was established. The Apriori algorithm, which is one of the popular algorithms of association rules mining, is utilized for the data analysis. Apriori algorithm was able to produce 72 rules which are accurate above 90%. It is thought that the produced rules can be of help in profiling the students, and they can contribute to work of school management, teachers, parents and students.

Keywords

• Data Mining,Association Rules,Student Profile,Data Warehouse

References

1. Agrawal, R., Imielinski, T. & Swami, A. (1993). ‘‘Mining Association Rules between Sets of Items in Large Databases’’. Acm sigmod record 22 (1993) 207-216.
2. Angeline, D. M. D. (2013). ‘‘Association rule generation for student performance analysis using apriori algorithm’’. The SIJ Transactions on Computer Science Engineering & its Applications (CSEA). 1 (2013) 12-16.
3. Aydemir, E. (2019). Geçme Notlarının Veri Madenciliği Yöntemleriyle Tahmin Edilmesi’’. European Journal of Science and Technology 15 (2019) 70-76. doi: https://doi.org/10.31590/ejosat.518899.
4. Birant, D., Kut, A., Ventura, M., Altınok, H., Altınok, B., Altınok, E., & Ihlamur, M. (2010). İş Zekâsı Çözümleri için Çok Boyutlu Birliktelik Kuralları Analizi’’. Akademik Bilişim 10 (2010) 256.
5. Bose, I., Chun, L. A.,Yue, L. V. W., Ines, L. H. W. & Helen, W. O. L. (2009). ‘‘Business Data Warehouse: The Case of Wal-Mart’’. Data Mining Applications for Empowering Knowledge Societies. Ed. Rahman H. Information Science Reference. (2009) 189-198. Bangladesh.
6. Bresfelean, V. P., Bresfelean, M., Ghisoiu, N., & Comes, C. A. (2008). ‘‘Determining students’ academic failure profile founded on data mining methods’’. ITI 2008-30th International Conference on Information Technology Interfaces (2008) 317-322. doi: 10.1109/ITI.2008.4588429
7. Gara, G. P. P., & Padao, F. R. F. (2015). ‘‘Mining Association Rules on Students Profiles and Personality Types’’. Proceedings of the International Multiconference of Engineers and Computer Scientists 1(2015) 307-312.
8. Giudici, P. & Figini S. (2008). “Applied Data Mining For Busıness and Industry”, A John Wiley and Sons, Ltd., Publication. 2008 90-91.

9. Huang, X., Xu, Y., Zhang, S., & Zhang, W. (2018). ‘‘Association rule mining for selecting proper students to take part in proper discipline competition: a case study of Zhejiang University of Finance and Economics’’. International Journal of Emerging Technologies in Learning (iJET) 13 (2018) 100-113.
10. Mohamad, S. K., & Tasir, Z. (2013). ‘‘Educational data mining: A review’’. Procedia-Social and Behavioral Sciences 97 (2013) 320-324.
11. Nahar J., Imam T., Tickle K. S., Chen Y. P. (2013). ‘‘Association Rule Mining to Detect Factors Which Contribute To Heart Disease in Males and Females’’. Expert Systems with Applications 40 (2013) 1086-1093. doi:https://doi.org/10.1016/j.eswa.2012.08.028.
12. Parack, S., Zahid, Z., & Merchant, F. (2012). ‘‘Application of data mining in educational databases for predicting academic trends and patterns’’. 2012 IEEE International Conference on Technology Enhanced Education (ICTEE) (2012) 1-4. doi: 10.1109/ICTEE.2012.6208617.
13. Rokach, Lior and Maimon, Oded (2008), Data Mining with Decision Trees, World Scientific, New Jersey.
14. Romero, C., & Ventura, S. (2007). ‘‘Educational data mining: A survey from 1995 to 2005’’. Expert systems with applications 33 (2007) 135-146.
15. Singh, C., Gopal, A., & Mishra, S. (2011). ‘‘Extraction and analysis of faculty performance of management discipline from student feedback using clustering and association rule mining techniques’’. 2011 3rd International Conference on Electronics Computer Technology 4 (2011) 94-96). Doi: 10.1109/ICECTECH.2011.5941864.
16. Taş, Y. (2018). Birliktelik Kuralları Madenciliği ve Bir Uygulama, Master’s Dissertation, Sivas Cumhuriyet University, Sivas 2018.
17. Webb, G. I. (2003). ‘‘Association Rules’’. Ed. Ye N. The Handbook Of Data Mining. (2003) 27-28. New Jersey.
18. Wu, T. & Li, X. (2003). ‘‘Data Storage and Management’’. Ed. Ye N. The Handbook of Data Mining. (2003) 393-407. New Jersey.
19. Zawayda, Y. I. A. (2013). ‘‘Mining postgraduate students' data using apriori algorithm’’. Doctoral dissertation. Universiti Utara,Malaysia