Detecting student frustration based on handwriting behavior

Hiroki Asai, Hayato Yamana

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

    6 Citations (Scopus)

    Abstract

    Detecting states of frustration among students engaged in learning activities is critical to the success of teaching assistance tools. We examine the relationship between a student's pen activity and his/her state of frustration while solving handwritten problems. Based on a user study involving mathematics problems, we found that our detection method was able to detect student frustration with a precision of 87% and a recall of 90%. We also identified several particularly discriminative features, including writing stroke number, erased stroke number, pen activity time, and air stroke speed.

    Original languageEnglish
    Title of host publicationUIST 2013 Adjunct - Adjunct Publication of the 26th Annual ACM Symposium on User Interface Software and Technology
    Pages77-78
    Number of pages2
    DOIs
    Publication statusPublished - 2013
    Event26th Annual ACM Symposium on User Interface Software and Technology, UIST 2013 - St. Andrews
    Duration: 2013 Oct 82013 Oct 11

    Other

    Other26th Annual ACM Symposium on User Interface Software and Technology, UIST 2013
    CitySt. Andrews
    Period13/10/813/10/11

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    Keywords

    • digital ink
    • learner tracking

    ASJC Scopus subject areas

    • Human-Computer Interaction
    • Software

    Cite this

    Asai, H., & Yamana, H. (2013). Detecting student frustration based on handwriting behavior. In UIST 2013 Adjunct - Adjunct Publication of the 26th Annual ACM Symposium on User Interface Software and Technology (pp. 77-78) https://doi.org/10.1145/2508468.2514718