Educational data mining (EDM) has been a rapidly growing research field over the last decade and enabled researchers to discover patterns and trends in education with more sophisticated methods. EDM offers promising solutions to complex educational problems. Given the rapid increase in the availability of big data in education and software programs to analyze big data, the demand for user-friendly, free software programs to implement EDM methods also continues to increase. The R programming language has become a popular environment for data mining due to its availability and flexibility. The rattle package in R contains a set of functions to implement data mining with a graphical user interface. This study demonstrates three widely used data mining algorithms (classification and regression tree, random forest, and support vector machine) in EDM using real data from the 2015 administration of the Programme for International Student Assessment (PISA). First, a brief introduction to EDM is provided along with the description of the selected data mining algorithms. Then, how to perform data mining analysis using the rattle ’s graphical user interface is demonstrated. The study concludes by comparing the results of the selected data mining algorithms and highlighting how those algorithms can be utilized in the context of educational research.

中文翻译：

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教育数据挖掘：R中的摇铃包教程

在过去的十年中，教育数据挖掘（EDM）一直是一个快速发展的研究领域，使研究人员能够通过更复杂的方法发现教育的模式和趋势。EDM为复杂的教育问题提供了有前途的解决方案。鉴于教育中大数据的可用性和用于分析大数据的软件程序的迅速增加，对实施EDM方法的用户友好的免费软件程序的需求也在不断增长。由于R编程语言的可用性和灵活性，它已成为一种流行的数据挖掘环境。R中的摇铃包包含一组函数，这些函数可通过图形用户界面实现数据挖掘。这项研究演示了三种广泛使用的数据挖掘算法（分类和回归树，随机森林，和支持向量机）在EDM中使用2015年国际学生评估计划（PISA）管理中的真实数据。首先，提供了对EDM的简要介绍以及所选数据挖掘算法的描述。然后，演示了如何使用拨浪鼓的图形用户界面执行数据挖掘分析。通过比较所选数据挖掘算法的结果并突出显示如何在教育研究的背景下利用这些算法，研究得出了结论。