Detecting student frustration based on handwriting behavior

Hiroki Asai, Hayato Yamana

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

8 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
Country/TerritoryUnited 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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