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 Nov 15
Event26th Annual ACM Symposium on User Interface Software and Technology, UIST 2013 - St. Andrews, United Kingdom
Duration: 2013 Oct 82013 Oct 11

Publication series

NameUIST 2013 Adjunct - Adjunct Publication of the 26th Annual ACM Symposium on User Interface Software and Technology

Conference

Conference26th Annual ACM Symposium on User Interface Software and Technology, UIST 2013
CountryUnited Kingdom
CitySt. Andrews
Period13/10/813/10/11

Keywords

  • digital ink
  • learner tracking

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Software

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  • 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). (UIST 2013 Adjunct - Adjunct Publication of the 26th Annual ACM Symposium on User Interface Software and Technology). https://doi.org/10.1145/2508468.2514718