Understand students’ self-reflections through learning analytics

Vitomir Kovanović, Srećko Joksimović, Negin Mirriahi, Ellen Blaine, Dragan Gašević, George Siemens, Shane Dawson

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

    63 Citations (Scopus)

    Abstract

    Reflective writing has been widely recognized as one of the most effective activities for fostering students’ reflective and critical thinking. The analysis of students’ reflective writings has been the focus of many research studies. However, to date this has been typically a very labor-intensive manual process involving content analysis of student writings. With recent advancements in the field of learning analytics, there have been several attempts to use text analytics to examine student reflective writings. This paper presents the results of a study examining the use of theoretically-sound linguistic indicators of different psychological processes for the development of an analytics system for assessment of reflective writing. More precisely, we developed a random-forest classification system using linguistic indicators provided by the LIWC and Coh-Metrix tools. We also examined what particular indicators are representative of the different types of student reflective writings.

    Original languageEnglish
    Title of host publicationProceedings of the 8th International Conference on Learning Analytics & Knowledge (LAK'18)
    Subtitle of host publicationTowards User-Centred Learning Analytics
    EditorsSimon Buckingham Shum, Rebecca Ferguson, Agathe Merceron, Xavier Ochoa
    Place of PublicationNew York NY USA
    PublisherAssociation for Computing Machinery (ACM)
    Pages389-398
    Number of pages10
    ISBN (Print)9781450364003
    DOIs
    Publication statusPublished - 7 Mar 2018
    EventInternational Learning Analytics & Knowledge Conference 2018 - SMC Conference & Function Centre, Sydney, Australia
    Duration: 5 Mar 20189 Mar 2018
    Conference number: 8th
    https://latte-analytics.sydney.edu.au/ (Conference website)
    https://dl.acm.org/doi/proceedings/10.1145/3170358 (Proceedings)

    Conference

    ConferenceInternational Learning Analytics & Knowledge Conference 2018
    Abbreviated titleLAK 2018
    Country/TerritoryAustralia
    CitySydney
    Period5/03/189/03/18
    Internet address

    Keywords

    • Learning analytics
    • Online learning
    • Self-reflections
    • Text mining

    Cite this