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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

A Comparison of EDM Tools and Techniques

Author 1: Eman Alshehri
Author 2: Hosam Alhakami
Author 3: Abdullah Baz
Author 4: Tahani Alsubait

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0111295

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 12, 2020.

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Abstract: Several higher educational institutions are adapting the strategy of predicting the student’s performance throughout the academic years. Such a practice ensures not only better academic outcomes but also helps the institutions to reorient their curriculums and teaching pedagogies so as to add to the students’ learning curve. Educational Data Mining (EDM) has risen as a useful technology in this league. EDM techniques are now being used for predicting the enrolment of students in a specific course, detection of any irregular grades, prediction about students’ performance, analyzing and visualizing of data, and providing feedback for overall improvement in the academic spheres. This paper reviews the studies related to EDM, including the approaches, data sets, tools, and techniques that have been used in those studies, and points out the most efficient techniques. This review paper uses true prediction accuracy as a standard for the comparison of different techniques for each EDM applications of the surveyed literature. The results show that the J48 and K-means are the most effective techniques for predicting the students’ performance. Furthermore, the results also cite that Bayesian and Decision Tree Classifiers are the most widely used techniques for predicting the students’ performance. In addition, this paper highlights that the most widely used tool was WEKA, with approximately 75% frequency. The present study’s empirical assessments would be a significant contribution in the domain of EDM. The comparison of different tools and techniques presented in this study are corroborative and conclusive, thus the results will prove to be an effective reference point for the practitioners in this field. As a much needed technological asset in the present day educational context, the study also suggests that additional surveys are recommended to be driven for each of the EDM applications by taking into account more standards to set the best techniques more accurately.

Keywords: Educational Data Mining (EDM); students’ performance; prediction; higher education; WEKA

Eman Alshehri, Hosam Alhakami, Abdullah Baz and Tahani Alsubait, “A Comparison of EDM Tools and Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 11(12), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111295

@article{Alshehri2020,
title = {A Comparison of EDM Tools and Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111295},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111295},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {12},
author = {Eman Alshehri and Hosam Alhakami and Abdullah Baz and Tahani Alsubait}
}


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