Exploring the potential of social annotations for predictive and descriptive analytics

Shaveen Singh

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

    Abstract

    In this paper, we illustrate the successful implementation of a social annotation tool within a content authoring platform, that allows students to discuss learning material with their fellow classmates, and to self-report on their cognitive, metacognitive and affective states-by self-coding the annotations as they journey through the learning material. We explore the predictive potential of such self- reports in reading material against the students completion rate and assessment scores, and also examine how visualisation of these annotation classifications can help instructors easily identify issues and adapt their teaching approach and learning material.
    Original languageEnglish
    Title of host publicationProceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education
    EditorsArnold Pears, Mihaela Sabin
    Place of PublicationNew York NY USA
    PublisherAssociation for Computing Machinery (ACM)
    Pages247-248
    Number of pages2
    ISBN (Electronic)9781450368957
    ISBN (Print)9781450363013
    DOIs
    Publication statusPublished - 2019
    EventAnnual Conference on Innovation and Technology in Computer Science Education 2019 - Aberdeen, United Kingdom
    Duration: 12 Jul 201917 Jul 2019
    Conference number: 24th
    https://iticse.acm.org/

    Conference

    ConferenceAnnual Conference on Innovation and Technology in Computer Science Education 2019
    Abbreviated titleITiCSE 2019
    CountryUnited Kingdom
    CityAberdeen
    Period12/07/1917/07/19
    Internet address

    Keywords

    • Distance learning
    • Collaborative learning

    Cite this

    Singh, S. (2019). Exploring the potential of social annotations for predictive and descriptive analytics. In A. Pears, & M. Sabin (Eds.), Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education (pp. 247-248). Association for Computing Machinery (ACM). https://doi.org/10.1145/3304221.3325547