The flipped classroom gives students the flexibility to organize their learning, while teachers can monitor their progress analyzing their online activity. In massive courses where there are a variety of activities, automated analysis techniques are required in order to process the large volume of information that is generated, to help teachers take timely and appropriate actions. In these scenarios, it is convenient to classify students into a small number of groups that can receive dedicated support. Using only online activity to group students has proven to be insufficient to characterize relevant groups, because of that this study proposes to understand differences in online activity using differences in course status and learning experience, using data from a programming course (n = 409). The model built shows that learning experience can be categorized in three groups, each with different academic performance and distinct online activity. The relationship between groups and online activity allowed us to build classifiers to detect students who are at risk of failing the course (AUC = 0.84) or need special support (AUC = 0.73), providing teachers with a useful mechanism for predicting and improving student outcomes.

中文翻译：

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使用在线活动在翻转课堂中提供指导的模型

翻转的教室使学生可以灵活地组织学习，而老师可以通过分析在线活动来监控他们的进度。在开展各种活动的大规模课程中，需要使用自动分析技术来处理所生成的大量信息，以帮助教师及时采取适当的行动。在这些情况下，将学生分类为可以接受专门支持的少数几个组非常方便。事实证明，仅使用在线活动对学生进行分组不足以描述相关人群的特征，因为该研究建议利用课程状态和学习经验的差异，通过编程课程的数据来理解在线活动的差异（n = 409）。建立的模型表明，学习经历可以分为三类，每组都有不同的学习成绩和独特的在线活动。小组与在线活动之间的关系使我们能够建立分类器，以检测可能面临课程失败（AUC = 0.84）或需要特殊支持（AUC = 0.73）的学生，从而为教师提供了一种预测和改善学生成绩的有用机制。