Visualizing the Educational Data Mining Literature

I. Papadogiannis, N. Platis, V. Poulopoulos, C. Vassilakis, George Lepouras, Manolis Wallace, G. Karountzou
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Abstract

This article provides a visualization of a literature review in students’ performance prediction using educational data mining (EDM) techniques for the period 2015-2019. The results of the review are presented concisely and simply with the use of diagrams. Various aspects of the literature are examined, such as the algorithms adopted, the type of results drawn, the educational setting of the application and the actual exploitation of the outcomes. Findings indicate that tertiary education dominates the EDM field; in contrast, the focus given to secondary and primary education is minimal.
可视化教育数据挖掘文献
本文对2015-2019年期间使用教育数据挖掘(EDM)技术预测学生成绩的文献综述进行了可视化分析。本文用图表简明扼要地介绍了评审结果。研究了文献的各个方面,如采用的算法、得出的结果类型、应用程序的教育设置和结果的实际利用。研究结果表明,高等教育在EDM领域占主导地位;相比之下,对中等教育和初等教育的关注是最少的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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