Data mining for Education Sector, a proposed concept

Ammar Salamh Mujali Al-Rawahnaa, Anas Yahya Bader Al Hadid
Pages 1-10
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DOI https://doi.org/10.47738/jads.v1i1.6

Abstract


Data mining is very much needed in various fields In accessing a large amount of data requires time and a high level of accuracy. In higher education the potential influence of data mining on the learning processes and outcomes of the students was realized. Especially in the field of education, knowing almost every educational institute, both public and private, has thousands of data from students with a variety of different programs and subjects. Understanding the benefits of data retrieval will facilitate the course of education itself. The use of Data mining in education will be useful in developing a student-focused strategy and in providing the correct tools that institutions would be able to use for quality improvement purposes. In this paper, we will find out the benefits of applying data mining in the education sector using classification, prediction, association and clustering methods.


Keywords


Data Analytics; Education; Educational Data Mining; Learning Analytics.

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References


[1]    A. A. Kardan, H. Sadeghi, S. S. Ghidary, and M. R. F. Sani, “Prediction of student course selection in online higher education institutes using neural network,” Comput. Educ., vol. 65, pp. 1–11, 2013, doi: 10.1016/j.compedu.2013.01.015.

[2]    L. M. Caesarius and J. Hohenthal, “Searching for big data: How incumbents explore a possible adoption of big data technologies,” Scand. J. Manag., vol. 34, no. 2, pp. 129–140, 2018, doi: 10.1016/j.scaman.2017.12.002.

[3]    M. S. Mahmud, J. Z. Huang, S. Salloum, T. Z. Emara, and K. Sadatdiynov, “A survey of data partitioning and sampling methods to support big data analysis,” Big Data Min. Anal., vol. 3, no. 2, pp. 85–101, 2020, doi: 10.26599/bdma.2019.9020015.

[4]    X. Chen and M. Xie, “A split-and-conquer approach for analysis of extraordinarily large data,” Statistica Sinica, vol. 24, no. 4, pp. 1655–1684, 2014.

[5]    Y. Baashar et al., “Customer relationship management systems (CRMS) in the healthcare environment: A systematic literature review,” Comput. Stand. Interfaces, vol. 71, no. March, p. 103442, 2020, doi: 10.1016/j.csi.2020.103442.

[6]    C. da S. Chagas, H. S. K. Pinheiro, W. de Carvalho Junior, L. H. C. dos Anjos, N. R. Pereira, and S. B. Bhering, “Data mining methods applied to map soil units on tropical hillslopes in Rio de Janeiro, Brazil,” Geoderma Reg., vol. 9, pp. 47–55, 2017, doi: 10.1016/j.geodrs.2017.03.004.

[7]    J. S. Challa, P. Goyal, S. Nikhil, A. Mangla, S. S. Balasubramaniam, and N. Goyal, “DD-Rtree: A dynamic distributed data structure for efficient data distribution among cluster nodes for spatial data mining algorithms,” Proc. - 2016 IEEE Int. Conf. Big Data, Big Data 2016, pp. 27–36, 2016, doi: 10.1109/BigData.2016.7840586.

[8]    C. S. Ishikiriyama, D. Miro, and C. F. S. Gomes, “Text mining business intelligence: A small sample of what words can say,” Procedia Comput. Sci., vol. 55, no. Itqm, pp. 261–267, 2015, doi: 10.1016/j.procs.2015.07.044.

[9]    J. R. Saura, “Using Data Sciences in Digital Marketing: Framework, methods, and performance metrics,” J. Innov. Knowl., 2020, doi: 10.1016/j.jik.2020.08.001.

[10]    G. M. Borkar, L. H. Patil, D. Dalgade, and A. Hutke, “A novel clustering approach and adaptive SVM classifier for intrusion detection in WSN: A data mining concept,” Sustain. Comput. Informatics Syst., vol. 23, pp. 120–135, 2019, doi: 10.1016/j.suscom.2019.06.002.

[11]    Y. Sato, K. Izui, T. Yamada, and S. Nishiwaki, “Data mining based on clustering and association rule analysis for knowledge discovery in multiobjective topology optimization,” Expert Syst. Appl., vol. 119, pp. 247–261, 2019, doi: 10.1016/j.eswa.2018.10.047.

[12]    L. Siguenza-Guzman, V. Saquicela, E. Avila-Ordóñez, J. Vandewalle, and D. Cattrysse, “Literature Review of Data Mining Applications in Academic Libraries,” J. Acad. Librariansh., vol. 41, no. 4, pp. 499–510, 2015, doi: 10.1016/j.acalib.2015.06.007.

[13]    Siemens, George, and Phil Long. "Penetrating the fog: Analytics in learning and education." EDUCAUSE review, vol. 46, no. 5, pp. 30, 2011. doi: 10.17471/2499-4324/195.

[14]    S. Natek and M. Zwilling, “Student data mining solution-knowledge management system related to higher education institutions,” Expert Syst. Appl., vol. 41, no. 14, pp. 6400–6407, 2014, doi: 10.1016/j.eswa.2014.04.024.

[15]    Q. Liu, F. Xiao, and Z. Zhao, “Grouting knowledge discovery based on data mining,” Tunn. Undergr. Sp. Technol., vol. 95, no. December 2018, p. 103093, 2020, doi: 10.1016/j.tust.2019.103093.

[16]    Ngai, Eric WT, Li Xiu, and Dorothy CK Chau. "Application of data mining techniques in customer relationship management: A literature review and classification." Expert systems with applications, vol. 36, no. 2, pp. 2592-2602, 2009. doi: 10.1016/j.eswa.2008.02.021

[17]    Aksenova, Svetlana S., Du Zhang, and Meiliu Lu. "Enrollment prediction through data mining." 2006 IEEE International Conference on Information Reuse & Integration. IEEE, pp. 510-515, 2006. doi: 10.1109/IRI.2006.252466

[18]    T. C. Hsia, A. J. Shie, and L. C. Chen, “Course planning of extension education to meet market demand by using data mining techniques - an example of Chinkuo technology university in Taiwan,” Expert Syst. Appl., vol. 34, no. 1, pp. 596–602, 2008, doi: 10.1016/j.eswa.2006.09.025.

[19]    Woo, Ji Young, Sung Min Bae, and Sang Chan Park. "Visualization method for customer targeting using customer map." Expert Systems with Applications, vol. 28, no. 4, pp. 763-772, 2005. doi: 10.1016/j.eswa.2004.12.041

[20]    Kardan, Ahmad A., et al. "Prediction of student course selection in online higher education institutes using neural network." Computers & Education, Vol. 65, pp. 1-11, 2013. doi: 10.1016/j.compedu.2013.01.015

[21]    Mardikyan, Sona, and Bertain Badur. "Analyzing Teaching Performance of Instructors Using Data Mining Techniques." Informatics in Education., vol. 10, no. 2, pp. 245-257, 2011. doi: 10.15388/infedu.2011.17

[22]    N. Tomasevic, N. Gvozdenovic, and S. Vranes, “An overview and comparison of supervised data mining techniques for student exam performance prediction,” Comput. Educ., vol. 143, no. February 2019, p. 103676, 2020, doi: 10.1016/j.compedu.2019.103676.




DOI: https://doi.org/10.47738/jads.v1i1.6

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Journal of Applied Data Sciences

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Website:http://bright-journal.org/JADS
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