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.
Highlights
A Comparison of Educational Data Mining (EDM) Tools and TechniquesAbstract—Several higher educational institutions are adapting the strategy of predicting the student’s performance throughout the academic years
Data mining is the most effective process for analyzing big data warehouses to derive valid and useful information, to extract hidden data, and to detect relationships between factors in massive data [1]
The Educational Data Mining (EDM) would help all the educational stakeholders in several ways. Such tools and techniques could support to improve the students’ performance and success in academics, leverage teachers’ performance, and support decision-making in institutions
Summary
Abstract—Several higher educational institutions are adapting the strategy of predicting the student’s performance throughout the academic years. 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 comparison of different tools and techniques presented in this study are corroborative and conclusive, 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 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
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