With the vast number of apps and the complexity of their features, it is becoming challenging for teachers to select a suitable learning app for their courses. Several evaluation frameworks have been proposed in the literature to assist teachers with this selection. The iPAC framework is a well-established mobile learning framework highlighting the learners’ experience of personalization, authenticity, and collaboration (iPAC). In this article, we introduce an approach to automate the identification and comparison of iPAC relevant apps. We experiment with natural language processing and machine learning techniques, using data from the app description and app reviews publicly available in app stores. We further empirically validate the keyword base of the iPAC framework based on the app users’ language in app reviews. Our approach automatically identifies iPAC relevant apps with promising results ( $F$ 1 score $\sim$ 72%) and evaluates them similarly as domain experts (Spearman's rank correlation 0.54). We discuss how our findings can be useful for teachers, students, and app vendors.

中文翻译：

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使用App Store数据自动评估教育应用程序

由于应用数量众多且功能复杂，教师为课程选择合适的学习应用正变得越来越困难。文献中提出了几种评估框架，以帮助教师进行这种选择。iPAC框架是一个完善的移动学习框架，突出显示了学习者的个性化，真实性和协作（iPAC）体验。在本文中，我们介绍了一种自动识别和比较与iPAC相关的应用程序的方法。我们使用应用程序描述中的数据和应用程序商店中公开提供的应用程序评论中的数据，对自然语言处理和机器学习技术进行实验。我们还将根据应用程序评论中应用程序用户的语言，以经验方式验证iPAC框架的关键字库。 $ F $ 1分 $ \ sim $ 72％），并像领域专家一样对它们进行评估（Spearman等级相关度为0.54）。我们讨论了我们的发现如何对教师，学生和应用程序供应商有用。