Predicting Students' Performance by Using Data Mining Methods

Authors

DOI:

https://doi.org/10.31642/JoKMC/2018/100202%20

Keywords:

Data Mining , E-Education, K-means Clustering, Rand Index, Traditional Education

Abstract

Abstract

The corona pandemic disrupted the educational process, especially in universities that use traditional education. Universities were therefore obliged to move from traditional education to e-education without adequate preparations. The aim of this research is to analyze the students' performance in the two educational environments and predict the result of any of them in the future. The k-means algorithm, an important data mining method, was used to analyze the results of the fourth-stage classes of five consecutive years of students from one Iraqi university's scientific departments. Four of these years were traditional education, while the last was E-education to see whether the student's performance distribution is normal or abnormal. The results indicate a 100 percent of students’ success rate in e-education, while the upper limit is 70 percent for the previous years.

Moreover, the average class rate increased to 75 percent compared to 62 in previous years.  The decision tree has been built based on a dataset created from the collected data to predict the distribution of both traditional and e-education with a 2% error tolerance. The study shows that using the exact mechanism in e-education will give abnormal results. Therefore, the study recommends the need for good infrastructure, the preparation of efficient staff, increasing students’ skills, and appropriate software platforms for an accurate assessment of students’ performance.

Downloads

Download data is not yet available.

References

J Han, m kamber, & j pei, .Data mining practical machine learning tools and techniques 3rd ed, Morgan Kaufmann Puplishers,Burrlington,2011.

[2] B Albreiki,, N Zaki,& H Alashwal, A systematic literature review of student’performance prediction using machine learning techniques ,Education Sciences ,VOL 11, NO 9,PP 552, MDPI,2021.

https://doi.org/10.3390/educsci11090552

N A Yassein, R G Helali,S B,& others, Predicting student academic performance in KSA using data mining techniques, Journal of Information Technology & Software Engineering, VOL 7, NO 5,PP 1-5,2017

W F Yaacob, WF Wan,N M Sobri, S M Nasir,N H Norshahidi,& WZ Wan,Predicting student drop-out in higher institution using data mining techniques, Journal of Physics: Conference Series},VOL 1496,NO 1,PP 012005,2020.

https://DOI 10.1088/1742-6596/1496/1/012005

H VanDer , M B Amanda ,& K Matthew,Improving decision making in school psychology: Making a difference in the lives of students, not just a prediction about their lives, School Psychology Review, VOL 47,NO 4, PP 385-395, National Association of School Psychologists 4340 East West Highway, Suite,2018.

Kansal, Tushar and Bahuguna, Suraj and Singh, Vishal and Choudhury,& Tanupriya ,Customer segmentation using K-means clustering,{2018 international conference on computational techniques, electronics and mechanical systems (CTEMS),PP 135-139,IEE,2018.

http//DOI: 10.1109/CTEMS.2018.8769171

A Iatrellis, I K Savvas, P Fitsilis,& V C Gerogiannis, A two-phase machine learning approach for predicting student outcomes, Education and Information Technologies,VOL 26,PP 69-88,Springer,2021

J Lee,Racial and ethnic achievement gap trends: Reversing the progress toward equity?,Educational researcher, VOL 31 ,NO 1, 3-12,PP 3-12, Sage Publications Sage CA: Thousand Oaks, CA,2002.

E Osmanbegovic, ,& M Suljic, Data mining approach for predicting student performance, Economic Review: Journal of Economics and Business, VOL 10, NO 1, PP 3-12, Tuzla: University of Tuzla, Faculty of Economics,2012

P Odeyar, B Apel, B Derek, R Hall, B Zon, & K Skrzypkowski, A Review of Reliability and Fault Analysis Methods for Heavy Equipment and Their Components Used in Mining, Energies, VOL 15, NO 17, MDPI,2022.

https://doi.org/10.3390/en15176263

Y Baashar, G Alkawsi, Gamal,N Ali, H Alhussian,& H T Bahbouh, Predicting student’s performance using machine learning methods: A systematic literature review}, 2021 International Conference on Computer & Information Sciences (ICCOINS),PP 357-362, IEEE,2021.

http//DOI: 10.1109/ICCOINS49721.2021.9497185

Y Baashar, G Alkawsi, A Mustafa, A A Alkahtani, Y A Alsariera, A Q Ali, Abdulrazzaq W Hashim, T Wahidah ,& S K Tiong, Toward predicting student’s academic performance using artificial neural networks (ANNs), Applied Sciences, VOL 12, NO 13, PP 1289, MDPI,2022. https://doi.org/10.3390/app12031289

B K Francis, & S S Babu, Predicting academic performance of students using a hybrid data mining approach , VOL 43, PP 1-15, Springer,2019

B. K. Verma ,N. and Srivastava &, H. S.K Singh,Prediction of Students’ Performance in e-Learning Environment using Data Mining/Machine Learning Techniques, vol 23,pp 586--593,2021.

D. Y. Mohammed, “The web-based behavior of online learning: An evaluation of different countries during the COVID-19 pandemic,” Advances in Mobile Learning Educational Research, vol. 2, no. 1, pp. 263–267, 2022. DOI 10.25082/AMLER.2022.01.010

S. M. De Oca, M. Villada-Balbuena, and C. Camacho-Zuniga, “Professors' concerns after the shift from face-to-face to online teaching amid covid-19 contingency: An educational data mining analysis,” 2021 Machine Learning-Driven Digital Technologies for Educational Innovation Workshop, 2021. DOI: 10.1109/IEEECONF53024.2021.9733778

A. Alsharhan, S. A. Salloum, & A. Aburayya, Using e-learning factors to predict student performance in the practice of precision education, Journal of Legal, Ethical and Regulatory Issues, vol 24, pp1--14, Jordan Whitney Enterprises, Inc,2021.

Downloads

Published

2023-08-31

How to Cite

Jassim, A., Al-Taie, A., & Naama, Z. S. (2023). Predicting Students’ Performance by Using Data Mining Methods. Journal of Kufa for Mathematics and Computer, 10(2), 10–15. https://doi.org/10.31642/JoKMC/2018/100202

Similar Articles

<< < 1 2 3 4 5 6 7 > >> 

You may also start an advanced similarity search for this article.