Student Profiles of Change in a University Course: A Complex Dynamical Systems Perspective

Oleksandra Poquet, Jelena Jovanovic, Abelardo Pardo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Learning analytics approaches to profiling students based on their study behaviour remain limited in how they integrate temporality and change. To advance this area of work, the current study examines profiles of change in student study behaviour in a blended undergraduate engineering course. The study is conceptualised through complex dynamical systems theory and its applications in psychological and cognitive science research. Students were profiled based on the changes in their behaviour as observed in clickstream data. Measure of entropy in the recurrence of student behaviour was used to indicate the change of a student state, consistent with the evidence from cognitive sciences. Student trajectories of weekly entropy values were clustered to identify distinct profiles. Three patterns were identified: stable weekly study, steep changes in weekly study, and moderate changes in weekly study. The students with steep changes in their weekly study activity had lower exam grades and showed destabilisation of weekly behaviour earlier in the course. The study investigated the relationships between these profiles of change, student performance, and other approaches to learner profiling, such as self-reported measures of self-regulated learning, and profiles based on the sequences of learning actions.

Original languageEnglish
Title of host publicationLAK 2023 Conference Proceedings - Towards Trustworthy Learning Analytics - 13th International Conference on Learning Analytics and Knowledge
PublisherAssociation for Computing Machinery
Pages197-207
Number of pages11
ISBN (Electronic)9781450398657
DOIs
StatePublished - 13 Mar 2023
Event13th International Conference on Learning Analytics and Knowledge: Towards Trustworthy Learning Analytics, LAK 2023 - Arlington, United States
Duration: 13 Mar 202317 Mar 2023

Publication series

NameACM International Conference Proceeding Series

Conference

Conference13th International Conference on Learning Analytics and Knowledge: Towards Trustworthy Learning Analytics, LAK 2023
Country/TerritoryUnited States
CityArlington
Period13/03/2317/03/23

Keywords

  • complex dynamical systems
  • learning analytics
  • self-regulated learning

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