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

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.