Using learner trace data to understand metacognitive processes in writing from multiple sources

Mladen Rakovic, Yizhou Fan, Joep Van Der Graaf, Shaveen Singh, Jonathan Kilgour, Lyn Lim, Johanna Moore, Maria Bannert, Inge Molenaar, Dragan Gasevic

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

Abstract

Writing from multiple sources is a commonly administered learning task across educational levels and disciplines. In this task, learners are instructed to comprehend information from source documents and integrate it into a coherent written composition to fulfil the assignment requirements. Even though educationally potent, multi-source writing tasks are considered challenging to many learners, in particular because many learners underuse monitoring and control, critical metacognitive processes for productive engagement in multi-source writing. To understand these processes, we conducted a laboratory study involving 44 university students. They engaged in multi-source writing task hosted in digital learning environment. Adding to previous research, we unobtrusively measured metacognitive processes using learners' trace data collected via multiple data channels and in both writing and reading space of the multi-source writing task. We further investigated how these processes affect the quality of a written product, i.e., essay score. In the analysis, we utilised both automatically and human-generated essay score. The rating performance of the essay scoring algorithm was comparable to that of human raters. Our results largely support the theoretical assumptions that engagement in metacognitive monitoring and control benefits the quality of written product. Moreover, our results can inform the development of analytics-based tools that support student writing by making use of trace data and automated essay scoring.

Original languageEnglish
Title of host publicationLAK22 Conference Proceedings
EditorsAlyssa Friend Wise, Roberto Martinez-Maldonado, Isabel Hilliger
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages130-141
Number of pages12
ISBN (Electronic)9781450395731
DOIs
Publication statusPublished - 2022
EventInternational Conference on Learning Analytics and Knowledge 2022: Learning Analytics for Transition, Disruption and Social Change - Online, United States of America
Duration: 21 Mar 202225 Mar 2022
Conference number: 12th
https://dl.acm.org/doi/proceedings/10.1145/3506860 (Proceedings)

Conference

ConferenceInternational Conference on Learning Analytics and Knowledge 2022
Abbreviated titleLAK 2022
Country/TerritoryUnited States of America
Period21/03/2225/03/22
Internet address

Keywords

  • monitoring
  • reading
  • semantic similarity
  • writing from multiple sources

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