Towards knowledge-transforming in writing argumentative essays from multiple sources: a methodological approach

Mladen Rakovic, Zahia Marzouk, Daniel Chang, Philip H. Winne

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

Abstract

Skillful essay writers successfully transform knowledge from multiple sources. However, when post-secondary writers draft essays after researching the articles, they often face challenges to engage in knowledge transforming, a complex process simultaneously involving reading comprehension, writing production and metacognitive monitoring (Bereiter & Scardamalia, 1987). We describe a two-facet methodological approach to model linguistic properties that distinguish knowledge-telling evidential sentences from knowledge- transforming ones in disciplinary argumentative writing. We collected and coded 40 post- secondary disciplinary argumentative essays based on an assigned argumentation framework and Bloom’s taxonomy (Sadker & Sadker, 2006). We use these coded argumentation schemes to develop a computational tool to generate writing analytics to scaffold writers towards more knowledge transforming processes.
Original languageEnglish
Title of host publicationCompanion Proceeding of the 9th International Conference on Learning Analytics & Knowledge (LAK’19)
EditorsChristopher Brooks, Rebecca Ferguson, Ulrich Hoppe
Place of PublicationNew York NY USA
PublisherSociety for Learning Analytics Research
Pages267-272
Number of pages6
Publication statusPublished - 2019
Externally publishedYes
EventInternational Learning Analytics & Knowledge Conference 2019 - Arizona State University, Tempe, United States of America
Duration: 4 Mar 20198 Mar 2019
Conference number: 9th
https://lak19.solaresearch.org/

Conference

ConferenceInternational Learning Analytics & Knowledge Conference 2019
Abbreviated titleLAK 2019
Country/TerritoryUnited States of America
CityTempe
Period4/03/198/03/19
Internet address

Keywords

  • Argumentation
  • writing
  • text analysis
  • knowledge telling
  • Knowledge transfer

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