In recent years, there is a lot of interest in modeling students' digital traces in Learning Management System (LMS) to understand students' learning behavior patterns including aspects of meta-cognition and self-regulation, with the ultimate goal to turn those insights into actionable information to support students to improve their learning outcomes. In achieving this goal, however, there are two main issues that need to be addressed given the existing literature. Firstly, most of the current work is course-centered (i.e. models are built from data for a specific course) rather than student-centered; secondly, a vast majority of the models are correlational rather than causal. Those issues make it challenging to identify the most promising actionable factors for intervention at the student level where most of the campus-wide academic support is designed for. In this paper, we explored a student-centric analytical framework for LMS activity data that can provide not only correlational but causal insights mined from observational data. We demonstrated this approach using a dataset of 1651 computing major students at a public university in the US during one semester in the Fall of 2019. This dataset includes students' fine-grained LMS interaction logs and administrative data, e.g. demographics and academic performance. In addition, we expand the repository of LMS behavior indicators to include those that can characterize the time-of-the-day of login (e.g. chronotype). Our analysis showed that student login volume, compared with other login behavior indicators, is both strongly correlated and causally linked to student academic performance, especially among students with low academic performance. We envision that those insights will provide convincing evidence for college student support groups to launch student-centered and targeted interventions that are effective and scalable.

翻译：近年来，建模学生在学习管理系统（LMS）中的数字轨迹备受关注，旨在理解学生元认知和自我调节等学习行为模式，最终将洞见转化为可行动信息以支持学生提升学习成果。然而，在实现这一目标过程中，现有文献存在两个主要问题需要解决。首先，当前大多数研究以课程为中心（即模型基于特定课程数据构建），而非以学生为中心；其次，绝大多数模型呈现的是相关性而非因果性。这些问题使得在为学生层面（校园范围学术支持的主要设计对象）识别最有前景的可干预因素时面临挑战。本文探索了一种以学生为中心的LMS活动数据分析框架，该框架不仅能从观测数据中挖掘相关性洞见，还能提供因果洞见。我们通过美国某公立大学2019年秋季学期1651名计算机专业学生的数据集验证了这一方法。该数据集包含学生细粒度的LMS交互日志以及人口统计学和学业表现等行政数据。此外，我们扩展了LMS行为指标库，纳入了能够表征登录时段特征（如时间类型）的指标。分析表明，与其他登录行为指标相比，学生登录频率与学业表现之间既存在强相关性，又具有因果联系——尤其对于学业表现较低的学生群体。我们预期这些洞见将为大学生支持团队提供有力证据，以开展有效且可扩展的以学生为中心的定向干预。