Handwriting Behavior as a Self-Confidence Discriminator

被引：2

作者：

Maruichi, Takanori ^{[1
]}

Uragami, Taichi ^{[1
]}

Vargo, Andrew ^{[1
]}

Kise, Koichi ^{[1
]}

机构：

[1] Osaka Prefecture Univ, Grad Sch Engn, Sakai, Osaka, Japan

来源：

UBICOMP/ISWC '20 ADJUNCT: PROCEEDINGS OF THE 2020 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2020 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS | 2020年

关键词：

mobile computing; quantified learning; handwriting behavior analysis; self-confidence estimation;

D O I：

10.1145/3410530.3414383

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Receiving feedback based on the combination of self-confidence and correctness of an answer can help learners to improve learning efficiency. In this study, we propose a self-confidence estimation method using a simple touch up/move/down events that can be measured in a classroom environment. We recorded handwriting behavior during the answering vocabulary questions with a tablet and a stylus pen, estimating self-reported confidence. We successfully built a method that can predict the user's self-confidence with a maximum of 73% accuracy.

引用

页码：78 / 81

页数：4

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