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Cited by 5Online Learning and AnalyticsEducation during COVID-19 pandemicOnline and Blended Learning

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Learning Management System Analytics to Examine the Behavior of Students in High Enrollment STEM Courses During the Transition to Online Instruction

Elliott, Rob, Luo, Xiao·2022·2022 IEEE Frontiers in Education Conference (FIE)
DOI 10.1109/fie56618.2022.9962732
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Abstract

The emergence of the COVID-19 pandemic resulted in the transition to near-total online instruction in early 2020. Several studies surveyed students about the impact of the pandemic on their behavior and engagement with their education; however, those studies may not include analysis of actual student behaviors. The field of learning analytics allows researchers to examine the records made while students interact with the educational technology tools that are commonly used to facilitate instruction in Institutes of Higher Education (IHEs). In most universities, delivery of many instructional materials is conducted via a Learning Management System (LMS). In this study, we describe our process to examine student interactions within the LMS to discover any measurable changes to student behavior during the pandemic.We examined the usage logs of the Canvas LMS at a large university in the midwestern US to examine the behavior of students' interactions with high-enrollment STEM courses in two semesters: one prior to and one during the pandemic. The log data was integrated with student demographic data so that the LMS behavior of subsets of students can be compared. Machine learning algorithms including clustering models and association rule mining were applied on the data. The results of this study demonstrate that the students' behaviors did change in the transition to online instruction. Students had more frequent sessions in the LMS on both computers and mobile devices, although…

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Metadata

Authors
Elliott, R., & Luo, X.
Affiliations
Indiana University – Purdue University Indianapolis (US)
ORCID
Venue
2022 IEEE Frontiers in Education Conference (FIE)
Publisher
IEEE
Published
2022-10-08
Pages
1-9
ISSN
0190-5848 (ISSN-L)
DOI
10.1109/fie56618.2022.9962732
OpenAlex
W4310354077
Cited by
5
References
19
Citations by year
2025: 3 · 2024: 1 · 2023: 1
FWCI
1.63
Type
conference paper
Language
en
Open access
closed
Licence
Locations
IEEE Frontiers in Education Conference (FIE) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Social Sciences › Education › Education during COVID-19 pandemic
  • Social Sciences › Education › Online and Blended Learning
Keywords
virtual learning environments, learning analytics, student behavior, online instruction, COVID-19 pandemic, Canvas LMS, mobile device use, student engagement, historically underrepresented students, educational equity, usage logs, clustering, association rule mining

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CoChat. (2026). Citation check: Learning Management System Analytics to Examine the Behavior of Students in High Enrollment STEM Courses During the Transition to Online Instruction. https://app.cochat.ai/papers/10.1109/fie56618.2022.9962732
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Elliott, R., & Luo, X. (2022). Learning Management System Analytics to Examine the Behavior of Students in High Enrollment STEM Courses During the Transition to Online Instruction. 2022 IEEE Frontiers in Education Conference (FIE). https://doi.org/10.1109/fie56618.2022.9962732