The focus of this study is the identification of reader profiles that differ in performance and progression in an educational literacy app. A total of 19,830 students in Grade 2 from 347 Elementary schools located in 30 different districts in the United States played the app from 2020 to 2021. Our aim was to identify unique groups of readers using an unsupervised statistical learning technique - cluster analysis. Six indicators generated from the students log files were included to provide insights into engagement and learning across four different reading-related skills: phonological awareness, early decoding, vocabulary, and comprehension processes. A key aim was to evaluate the implementation and performance of Gaussian mixture models, k-means, k-medoids, clustering large applications and hierarchical clustering, alongside provision of detailed guidance that can benefit researchers in the field. K-means algorithm performed the best and identified nine groups of readers. Children with low initial reading ability showed greater engagement with code-related games (phonological awareness, early decoding) and took longer to master these games, whereas children with higher initial ability showed more engagement with meaning-related games (vocabulary, comprehension processes). Our findings can inform further research that aims to understand individual differences in learning behaviour within digital environments both over time and across various cohorts of children.

中文翻译：

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应用聚类分析通过数字阅读补充品的参与来识别不同的读者群体

本研究的重点是识别教育素养应用程序中表现和进度不同的读者档案。 2020 年至 2021 年，来自美国 30 个不同地区 347 所小学的 19,830 名二年级学生玩了该应用程序。我们的目标是使用无监督统计学习技术（聚类分析）来识别独特的读者群体。其中包括从学生日志文件生成的六个指标，以提供对四种不同阅读相关技能的参与和学习的见解：语音意识、早期解码、词汇和理解过程。一个关键目标是评估高斯混合模型、k-means、k-medoids、大型应用程序聚类和层次聚类的实施和性能，同时提供可以使该领域的研究人员受益的详细指导。 K-means 算法表现最好，并识别出九组读者。初始阅读能力较低的儿童更多地参与与代码相关的游戏（语音意识、早期解码），并且需要更长的时间来掌握这些游戏，而初始阅读能力较高的儿童则更多地参与与意义相关的游戏（词汇、理解过程）。我们的研究结果可以为进一步的研究提供信息，这些研究旨在了解数字环境中随着时间的推移和不同儿童群体的学习行为的个体差异。