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，建模学习管理系统（LMS）中学生数字痕迹以理解其学习行为模式（包括元认知和自我调节方面）引起了广泛关注，其最终目标是将这些见解转化为可操作的信息，从而支持学生提升学习成果。然而，在实现这一目标的过程中，现有文献表明需解决两个主要问题：首先，当前大部分研究以课程为中心（即基于特定课程数据构建模型），而非以学生为中心；其次，绝大多数模型基于相关性而非因果性。这些问题使得在校园级学术支持主要针对学生群体设计的背景下，难以识别出最有望在个体层面实施干预的可操作因素。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见解，还能提供因果性洞察。我们通过一所美国公立大学2019年秋季学期1651名计算机专业学生的数据集验证了该方法，该数据集包含学生细粒度的LMS交互日志及人口统计学特征、学业成绩等行政数据。此外，我们扩展了LMS行为指标库，纳入了可表征登录时段特征（如睡眠类型）的指标。分析表明，相较于其他登录行为指标，学生登录行为总量与学生学业成绩（尤其是低学业表现学生群体）既存在强相关性，又具有因果关联。我们预计这些见解将为高校学生支持团队开展以学生为中心、兼具有效性与可扩展性的精准干预提供有力证据。