Modeling consistency using engagement patterns in online courses

Jianing Zhou, Suma Bhat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Consistency of learning behaviors is known to play an important role in learners' engagement in a course and impact their learning outcomes. Despite significant advances in the area of learning analytics (LA) in measuring various self-regulated learning behaviors, using LA to measure consistency of online course engagement patterns remains largely unexplored. This study focuses on modeling consistency of learners in online courses to address this research gap. Toward this, we propose a novel unsupervised algorithm that combines sequence pattern mining and ideas from information retrieval with a clustering algorithm to first extract engagement patterns of learners, represent learners in a vector space of these patterns and finally group them into groups with similar consistency levels. Using clickstream data recorded in a popular learning management system over two offerings of a STEM course, we validate our proposed approach to detect learners that are inconsistent in their behaviors. We find that our method not only groups learners by consistency levels, but also provides reliable instructor support at an early stage in a course.

Original languageEnglish (US)
Title of host publicationLAK 2021 Conference Proceedings - The Impact we Make
Subtitle of host publicationThe Contributions of Learning Analytics to Learning, 11th International Conference on Learning Analytics and Knowledge
PublisherAssociation for Computing Machinery
Pages226-236
Number of pages11
ISBN (Electronic)9781450389358
DOIs
StatePublished - Apr 12 2021
Externally publishedYes
Event11th International Conference on Learning Analytics and Knowledge: The Impact we Make: The Contributions of Learning Analytics to Learning, LAK 2021 - Virtual, Online, United States
Duration: Apr 12 2021Apr 16 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference11th International Conference on Learning Analytics and Knowledge: The Impact we Make: The Contributions of Learning Analytics to Learning, LAK 2021
Country/TerritoryUnited States
CityVirtual, Online
Period4/12/214/16/21

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Keywords

  • Behavior modeling
  • Cluster
  • Consistency analysis

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