CTAM4SRL: A consolidated temporal analytic method for analysis of Self-Regulated Learning

Debarshi Nath, Dragan Gasevic, Yizhou Fan, Ramkumar Rajendran

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

Abstract

Temporality in Self-Regulated Learning (SRL) has two perspectives: one as a passage of time and the other as an ordered sequence of events. Each of these conceptions is distinct and requires independent considerations. Only a single analytic method is not sufficient in adequately capturing both these facets of temporality. Yet, most research uses a single method in temporally-focused SRL research, and those that use multiple methods do not address both aspects of temporality. We propose CTAM4SRL, a consolidated temporal analytic method which combines advanced data visualisation, network analysis and pattern mining to capture both facets of temporality. We employ CTAM4SRL in a cohort of 36 learners engaged in a reading-writing activity. Using CTAM4SRL, we were able to provide a rich temporal explanation of the interplay of the self-regulatory processes of the learners. We were further able to identify differences in SRL behaviours in high and low performers in terms of their approach to learning comprising deep and surface strategies. High performers were able to more selectively and strategically combine deep and surface learning strategies when compared to low scorers- a behaviour which was only hypothesised in SRL literature previously, but now has empirical support provided by our consolidated analytic method.

Original languageEnglish
Title of host publicationLAK 2024 Conference Proceedings - The Fourteenth International Conference on Learning Analytics & Knowledge
EditorsSrecko Joksimovic, Andrew Zamecnik
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages645-655
Number of pages11
ISBN (Electronic)9798400716188
DOIs
Publication statusPublished - 2024
EventInternational Learning Analytics & Knowledge Conference 2024 - Kyoto, Japan
Duration: 18 Mar 202422 Mar 2024
Conference number: 14th
https://dl.acm.org/doi/proceedings/10.1145/3636555 (Conference Proceedings)
https://www.solaresearch.org/events/lak/lak24/
https://ceur-ws.org/Vol-3667/ (LAK 2024 Workshop Proceedings)

Conference

ConferenceInternational Learning Analytics & Knowledge Conference 2024
Abbreviated titleLAK 2024
Country/TerritoryJapan
CityKyoto
Period18/03/2422/03/24
Internet address

Keywords

  • Learning Analytics
  • Ordered Network Analysis
  • Pattern Mining
  • Self-Regulated Learning
  • Visualisation Techniques

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