Detecting learner's to-be-forgotten items using online handwritten data

Hiroki Asai, Hayato Yamana

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    An effective learning system is indispensable for human beings with a limited life span. Traditional learning systems schedule repetition based on both the results of a recall test and learning theories such as the spacing effect. However, there is room for improvement from the perspective of remembrance-level estimation. In this paper, we focus on on-line handwritten data obtained from handwriting using a computer. We collected handwritten data from remembrance tests to both analyze the problem of traditional estimation methods and to build a new estimation model using handwritten data as the input data. The evaluation found that our proposed model can output a continuous remembrance-level value of zero to 1, whereas traditional methods output a only binary decision. In addition, the experiment showed that our proposed model achieves the best performance with an F-value of 0.69.

    Original languageEnglish
    Title of host publicationCHINZ 2015 - Proceedings of the 15th New Zealand Conference on Human-Computer Interaction
    PublisherAssociation for Computing Machinery
    Pages17-20
    Number of pages4
    ISBN (Print)9781450336703
    DOIs
    Publication statusPublished - 2015 Sep 3
    Event15th New Zealand Conference on Human-Computer Interaction, CHINZ 2015 - Hamilton, New Zealand
    Duration: 2015 Sep 32015 Sep 4

    Other

    Other15th New Zealand Conference on Human-Computer Interaction, CHINZ 2015
    CountryNew Zealand
    CityHamilton
    Period15/9/315/9/4

    Keywords

    • Digital Ink
    • Handwriting
    • ITS
    • Language Learning
    • Rote Learning

    ASJC Scopus subject areas

    • Human-Computer Interaction
    • Computer Networks and Communications
    • Computer Vision and Pattern Recognition
    • Software

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  • Cite this

    Asai, H., & Yamana, H. (2015). Detecting learner's to-be-forgotten items using online handwritten data. In CHINZ 2015 - Proceedings of the 15th New Zealand Conference on Human-Computer Interaction (pp. 17-20). Association for Computing Machinery. https://doi.org/10.1145/2808047.2808049