Self-confidence Estimation on Vocabulary Tests with Stroke-level Handwriting Logs

被引:1
|
作者
Maruichi, Takanori [1 ]
Ishimaru, Shoya [2 ]
Kise, Koichi [1 ]
机构
[1] Osaka Prefecture Univ, Sakai, Osaka, Japan
[2] German Res Ctr Artificial Intelligence, Kaiserslautern, Germany
来源
2019 INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION WORKSHOPS (ICDAR 2019 WORKSHOP) AND 2ND INTERNATIONAL WORKSHOP ON HUMAN-DOCUMENT INTERACTION, VOL 3 | 2019年
关键词
quantified learning; stroke-level handwriting analysis; self-confidence estimation;
D O I
10.1109/ICDARW.2019.20047
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A user's self-confidence is as important as the correctness of the answer. Giving feedback based on self-confidence will enable the user to review unacquired knowledge more efficiently. In this paper, we propose a self-confidence estimation method based on stroke-level handwriting behavior. We consider two different handwriting answer formats: the format with character writing box and the format without it (free format). We have conducted the experiments to compare the performance on the dataset recorded with these two formats. We successfully built a method that can estimate the user's self-confidence with maximum 80% accuracy in the dataset with character writing box, 79% in the dataset in the free format.
引用
收藏
页码:18 / 22
页数:5
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