Educational Data Mining To Improve The Academic Performance in Higher Education

  • Alyaa A. Mahdi Cihan University-Erbil, Erbil, Kurdistan Region, Iraq.
Keywords: Data Mining, Educational Data Mining, Moodle, Learning Analytical Enhance Rubric

Abstract

Globalization and Innovation are mainly consider the great interest public sector and private business in the world especially in the higher education institutions. Educational Data Mining is mainly one of the business processes nowadays that attempt to bring the global innovation through improving and enhancing their processes and procedures to fulfill all the requirements and needs of the students as well as the institutions. The Educational Data Mining considered mostly concern with any research concerning the applications of the data mining and developing innovative techniques for data mining (DM) in the educational sector. This study mainly combined the use of the powerful online E-learning management system (Moodle) with data mining tools to improve the performance and effectiveness of the learning and teaching manners by using the innovative daily data that collected from the educational institutions.

Downloads

Download data is not yet available.

References

A. Dutt, M. A. Ismail and T. Herawan. A Systematic Review on Educational Data Mining. United States: IEEE Acess, 2017.

M. Chalaris, S. Gritzalis, M. Maragoudakis, C. Sgouropoulou and A. Tsolakidis. Improving quality of educational processes providing new knowledge using data mining techniques. Procedia-Social and Behavioral Sciences, vol. 147, pp. 390-397, 2014.

H. Aldowah, H. Al-Samarraie and W. M. Fauzy. Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, vol. 37, pp. 13-49, 2019.

S. Hussain, R. Najoua and N. A. Dahan. Educational Data Mining and Analysis of Students’ Academic Performance Using WEKA. Berlin, Germany: ResearchGate, 2018.

Z. Papamitsiou, A. Economides and I. N. Sneddon. Learning Analytics with Excel Pivot Tables. Berlin, Germany: Moodle Research Conference Beuth University of Applied Sciences, pp. 115-121, 2012.

C. Romrto, S. Ventura, and E. Garcıa. Data mining in course management systems: Moodle case study and tutorial. Computers and Education, vol. 51, no. 1, pp. 368-384, 2008.

M. N. Injadat, A. Moubayed, A. B. Nassif and A. Shami. Systematic Ensemble Model Selection Approach for Educational Data Mining. Netherlands: Elesiver, 2020.

R. S. Baker, Challenges for the Future of Educational Data Mining: The Baker Learning Analytics Prizes. Journal of Educational Data Mining, vol. 11, no. 1, pp. 1-17, 2019.

M. Petropoulou, K. Kasimatis, I. Dimopoulos and S. Retalis. LAe-R: A new learning analytics tool in Moodle for assessing students’ performance. Bulletin of the Technical Committee on Learning Technology, vol. 16, no. 1, pp. 2-5, 2014.

J. Hung, K. Rice, and A. Saba. An educational data mining model for online teaching and learning. Journal of Educational Technology Development and Exchange, vol. 5, no. 2, pp. 77-94, 2012.

A. Sahay, and K. Mehta. Assisting Higher Education in Assessing, Predicting, and Managing Issues Related to Student Success: A Web-based Software using Data Mining and Quality Function Deployment. Las Vegas: Academic and Business Research Institute Conference, 2010.

A. Saa. Educational data mining and students’ performance prediction. International Journal of Advanced Computer Science and Applications, vol. 7, no. 5, p. 070531, 2016.

B. K. Baradwaj and S. Pal. Mining Educational Data to Analyze Students Performance. International Journal of Advanced Computer Science and Applications, vol. 2, no. 6, pp. 63-69, 2011.

I. Harb. Higher Education and the Future of Iraq. United States: Institute of Peace, 2008.

H. F. Hasan, M. Nat and V. Z. Vanduhe. Gamified Collaborative Environment in Moodle. United States: IEEE Access, 2019.

B. Ngom, I. Niang, and H. Guillermet. Enhancing Moodle for Offline Learning in a Degraded Connectivity Environment.Tangier, Morocco: IEEE, 2012.

H. Dierenfeld, and A. Merceron. Learning Analytics with Excel Pivot Tables. Berlin, Germany: Moodle Research Conference, Beuth University of Applied Sciences, pp. 115-121, 2012.

V. Z. Vanduhe, M. Nat and H. F. Hasan. Continuance Intentions to Use Gamification for Training in Higher Education: Integrating the Technology Acceptance Model (TAM), Social Motivation, and Task Technology Fit (TTF). United States: IEEE, 2020.

Published
2020-12-20
How to Cite
1.
Mahdi A. Educational Data Mining To Improve The Academic Performance in Higher Education. cuesj [Internet]. 20Dec.2020 [cited 19Apr.2024];4(2):13-8. Available from: https://journals.cihanuniversity.edu.iq/index.php/cuesj/article/view/269
Section
Research Article