Detecting student frustration based on handwriting behavior

Authors:
Hiroki Asai;Hayato Yamana
Affiliations:
Waseda University, Shinjuku-ku, Japan;Waseda University, Shinjuku-ku, Japan
Venue:
Proceedings of the adjunct publication of the 26th annual ACM symposium on User interface software and technology
Year:
2013

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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.