Flipped classrooms supported by learning management systems (LMS) have been widely adopted by educational institutions. However, earlier studies have found problems with interpreting LMS log data to understand student approaches to learning within the context of a learning design. This study investigates whether it is possible to use LMS log data as a proxy to understand students' learning strategies over different periods of time in the flipped-classroom context. A total of 135 sophomores from two classes of a flipped programming course participated in this study. Exploratory factor analysis is first conducted on the log data to synthesize second-order predictors based on the total-effort model. Then, we investigate the extent to which these second-order predictors relate to students' learning outcomes over time. Four types of learning outcomes are considered, including a quiz, a midterm exam, a final exam and the final grade. For each type of learning outcome, multiple linear regression is used to construct a weekly prediction model from these predictors. Adjusted R-squared and RMSE (Root Mean Square Error) are the metrics used to compare the models. The results show that consistent second-order predictors can be derived from log data, implying that students' clicking events in LMS could manifest students' learning strategies understandable in the design context of a flipped classroom. Furthermore, compared with the first-order models, most of the models constructed using the second-order predictors have higher predictive performance, although with lower data fitness. In addition, the predictive performance of the models with MSLQ (Motivated Strategies for Learning Questionnaire) indicators and past assessment data are also examined. It is found that MSQL variables have a positive but short-termed effect on the models’ predictive ability, while past assessment data greatly improve the models of all types of learning outcomes. Theoretical contributions and implications of the proposed approach for practice, research and future research are discussed.

由学习管理系统（LMS）支持的翻转教室已被教育机构广泛采用。但是，较早的研究发现在解释LMS日志数据以了解学生在学习设计的背景下进行学习的方法方面存在问题。这项研究调查了是否有可能使用LMS日志数据作为代理来了解学生在翻转课堂环境下不同时间段的学习策略。来自两门翻转编程课程的总共135位二年级学生参加了这项研究。首先对日志数据进行探索性因素分析，以基于总努力模型合成二阶预测变量。然后，我们调查这些二阶预测变量与学生的学习成果相关的程度。考虑了四种学习结果，包括测验，期中考试，期末考试和期末成绩。对于每种类型的学习结果，使用多元线性回归从这些预测变量构建每周预测模型。调整后的R平方和RMSE（均方根误差）是用于比较模型的指标。结果表明，可以从日志数据中得出一致的二阶预测变量，这表明LMS中的学生点击事件可以表明学生的学习策略在翻转教室的设计环境中是可以理解的。此外，与一阶模型相比，使用二阶预测变量构造的大多数模型具有较高的预测性能，但数据适应性较低。此外，还检查了具有MSLQ（学习调查问卷的动机策略）指标和过去评估数据的模型的预测性能。结果发现，MSQL变量对模型的预测能力具有积极但短期的影响，而过去的评估数据极大地改善了所有类型学习成果的模型。讨论了所提出的方法在实践，研究和未来研究中的理论贡献和意义。