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A Feasibility Study on using Clustering Algorithms in Programming Education Research

机译：使用聚类算法在编程教育研究中的可行性研究

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Designing an experiment for programming education research, in which collecting and interpreting a large number of qualitative data about programmers is required, needs careful consideration in order to validate the experiment. When it comes to finding a pattern in the programming behaviour of a specific group of programmers (e,g. novice, intermediate or expert programmers), one of the critical issues is the selection of similar participants who can be placed in one group. In this study we were interested in finding a method that could shorten the path to finding participants. Therefore, the use of clustering algorithms to group similar participants was put to test in order to investigate the effectiveness and feasibility of this approach. The clustering algorithms that were used for this study were K-means and DBSCAN. The results showed that the use of these algorithms, for the mentioned purpose, is feasible and that both algorithms can identify similar participants and place them in the same group while participants who are not similar to others, and therefore are not the correct subject of the study, are recognised.
机译：设计用于编程教育研究的实验，其中需要收集和解释关于程序员的大量定性数据，需要仔细考虑以验证实验。在找到特定程序的编程行为中的模式（例如，新手，中级或专家程序员），其中一个关键问题是选择可以放在一个组中的类似参与者。在这项研究中，我们有兴趣找到一种可以缩短寻找参与者路径的方法。因此，将聚类算法用于分组类似参与者进行测试，以便调查这种方法的有效性和可行性。用于本研究的聚类算法是K-Means和DBSCAN。结果表明，使用这些算法，用于提到的目的，是可行的，并且两个算法都可以识别类似的参与者，并将它们放在同一个组中，而与其他人不相似的参与者，因此不是正确的主题研究，被认可。

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