Cover page and Table of Contents: PDF (size: 918KB)
Full Text (PDF, 918KB), PP.1-13
Views: 0 Downloads: 0
Clustering, Quality of Learning, Educational Data Mining, Clustering Algorithms, Students' Performance
The field of using Data Mining (DM) techniques in educational environments is typically identified as Educational Data Mining (EDM). EDM is rapidly becoming an important field of research due to its ability to extract valuable knowledge from various educational datasets. During the past decade, an increasing interest has arisen within many practical studies to study and analyze educational data especially students’ performance. The performance of students plays a vital role in higher education institutions. In keeping with this, there is a clear need to investigate factors influencing students’ performance. This study was carried out to identify the factors affecting students’ academic performance. K-means and X-means clustering techniques were applied to analyze the data to find the relationship of the students' performance with these factors. The study finding includes a set of the most influencing personal and social factors on the students’ performance such as parents’ occupation, parents’ qualification, and income rate. Furthermore, it is contributing to improving the education quality, as well as, it motivates educational institutions to benefit and discover the unseen patterns of knowledge in their students' accumulated data.
Mohammed Abdullah Al-Hagery, Maryam Abdullah Alzaid, Tahani Soud Alharbi, Moody Abdulrahman Alhanaya, "Data Mining Methods for Detecting the Most Significant Factors Affecting Students’ Performance", International Journal of Information Technology and Computer Science(IJITCS), Vol.12, No.5, pp.1-13, 2020. DOI:10.5815/ijitcs.2020.05.01
A. Dutt, “Clustering Algorithms Applied in Educational Data Mining,” Int. J. Inf. Electron. Eng., 2015.
A. Dutt, M. A. Ismail, and T. Herawan, “A Systematic Review on Educational Data Mining,” IEEE Access, vol. 5. Institute of Electrical and Electronics Engineers Inc., pp. 15991–16005, 2017.
G. Javidi, L. Rajabion, and E. Sheybani, “Educational Data Mining and Learning Analytics: Overview of Benefits and Challenges,” in Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017, 2018, pp. 1102–1107.
A. Abu Saa, M. Al-Emran, and K. Shaalan, “Factors Affecting Students’ Performance in Higher Education: A Systematic Review of Predictive Data Mining Techniques,” Technol. Knowl. Learn., 2019.
A. Badr El Din Ahmed and I. Sayed Elaraby, “Data Mining: A prediction for Student’s Performance Using Classification Method,” World J. Comput. Appl. Technol., vol. 2, no. 2, pp. 43–47, 2014.
J. J. Manoharan, S. H. Ganesh, M. L. P. Felciah, and A. K. S. Banu, “Discovering students’ academic performance based on GPA using K-means clustering algorithm,” in Proceedings - 2014 World Congress on Computing and Communication Technologies, WCCCT 2014, pp. 200–202.
R. Asif, A. Merceron, S. A. Ali, and N. G. Haider, “Analyzing undergraduate students’ performance using educational data mining,” Comput. Educ., vol. 113, pp. 177–194, Oct. 2017.
D. Aggarwal and D. Sharma “Application of Clustering for Student Result Analysis", vol. 7, Apr. 2019.
J. Kong, J. Han, J. Ding, H. Xia, and X. Han, “Analysis of students’ learning and psychological features by contrast frequent patterns mining on academic performance,” Neural Computing and Applications, Springer London, 2018.
M. Durairaj and C. Vijitha, “Educational Data mining for Prediction of Student Performance Using Clustering Algorithms,” 2014.
A. M. Dahie, A. Abdi, M. (Alleges, A. Abshir, and W. (Suldan, “Factors Affecting Student Academic Performance: Case Study from the University of Somalia in Mogadishu-Somalia,” IOSR J. Humanit. Soc. Sci. (IOSR-JHSS, vol. 23, no. 3, pp. 73–80, 2018.
A. Abu, “Educational Data Mining & Students’ Performance Prediction,” Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 5, 2016.
M. Ilic, P. Spalevic, M. Veinovic, and W. Saed Alatresh, “Students’ success prediction using Weka tool,” 2016.
S. Syahira, A. Tarmizi, S. Mutalib, N. Hamimah, A. Hamid, and S. A. Rahman, “Modern Education and Computer Science,” Mod. Educ. Comput. Sci., vol. 8, pp. 1–14, 2019.
V. Mhetre and M. Nagar, “Classification based data mining algorithms to predict slow, average and fast learners in educational system using WEKA,” in Proceedings of the International Conference on Computing Methodologies and Communication, ICCMC 2017, 2018.
K. Govindasamy and T. Velmurugan, “Analysis of Student Academic Performance Using Clustering Techniques.” (2018).
S. Kadiyala and C. Srinivas Potluri, “Analyzing the Student’s Academic Performance by using Clustering Methods in Data Mining,” 2014
O. Tokunbo Olufemi, A. Adekunle Adediran, and W. Oyediran, “factors affecting students’ academic performance in colleges of education in southwest, nigeria,” 2018.
A. R. Chordiya and S. B. Bagal, "Comparative Research of Clustering Algorithms for Prediction of Academic Performance of Students." Int. J. Eng. Res. Technol 4.1 (2015): 243-246.
F. J. Kaunang and R. Rotikan, "Students' Academic Performance Prediction using Data Mining," 2018 Third International Conference on Informatics and Computing (ICIC), Palembang, Indonesia, 2018, pp. 1-5, doi: 10.1109/IAC.2018.8780547.
K. Sya’iyah, H. Yuliansyah, and I. Arfiani, “Clustering Student Data Based On K-Means Algorithms,” Int. J. Sci. Technol. Res., vol. 8, p. 8, 2019.
T. Devasia, T. P. Vinushree, and V. Hegde,” Prediction of students performance using Educational Data Mining,” in Proceedings of 2016 International Conference on Data Mining and Advanced Computing, SAPIENCE 2016, 2016.
Confluence Veranstaltung 6. 2016 Noida et al., Proceedings of the 2016 6th International Conference Cloud System and Big Data Engineering (Confluence) 14 -15 January 2016, Amity University, Uttar Pradesh, Noida, India. IEEE, 2016.
S. J. S. Alawi, I. N. M. Shaharanee, and J. M. Jamil, “Profiling Oman education data using data mining approach,” in AIP Conference Proceedings, 2017, vol. 1891.
M. Durairaj and C. Vijitha,“Educational Data mining for Prediction of Student Performance Using Clustering Algorithms.” (2014).
S. Y. Chen and X. Liu, “The contribution of data mining to information science,” J. Inf. Sci., vol. 30, no. 6, pp. 550–558, 2004.
A. Aghababyan, N. Lewkow, and R. S. Baker, (2018) Enhancing the Clustering of Student Performance Using the Variation in Confidence. In: Nkambou R., Azevedo R., Vassileva J. (eds) Intelligent Tutoring Systems. ITS 2018. Lecture Notes in Computer Science, vol 10858. Springer, Cham. https://doi.org/10.1007/978-3-319-91464-0_27
R. S. Yadav, “Application of hybrid clustering methods for student performance evaluation,” Int. J. Inf. Technol., Apr. 2018.
A. M. de Morais, J. M. F. R. Araújo and E. B. Costa, “Monitoring Student Performance Using Data Clustering and Predictive Modelling,” FIE : 2014 IEEE Frontiers in Education Conference : proceedings : 22-25 October 2014.
P. V. R. Periyasamy, and V. Sugasini, “Analysis of Student Result Using Clustering Techniques.” (2014).
M. Tabrez Nafis and S. Taha Owais, “Students Academic Performance Using Partitioning Clustering Algorithms,” Int. J. Adv. Res. Comput. Sci., vol. 8, no. 5.
P. Naik, R. Shaikh, O. Diukar, S. Dessai, and P. S. B. Project Guide], “Predicting Student Performance Based On Clustering and Classification,” IOSR J. Comput. Eng., vol. 19, no. 03, pp. 49–52, Jun. 2017.
A. Mueen, B. Zafar, and U. Manzoor, “Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques,” Int. J. Mod. Educ. Comput. Sci., vol. 8, no. 11, pp. 36–42, Nov. 2016.
S. Lailiyah, E. Yulsilviana, and R. Andrea, “Clustering analysis of learning style on anggana high school student,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 17, no. 3, p. 1409, Jun. 2019.
N.Valarmathy and S.Krishnaveni “Performance Evaluation and Comparison of Clustering Algorithms used in Educational Data Mining.” vol. 7, Apr. 2019.
P. Bholowalia and A. Kumar, “EBK-Means: A Clustering Technique based on Elbow Method and K-Means in WSN,” 2014.