Student-centric Model of Learning Management System Activity and Academic Performance: from Correlation to Causation
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2022-10-27
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Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
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Abstract
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 (i.e. models are built taking the perspective of students by
analyzing data across courses); 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.