  Not only correctness but also self-confidence play an important role in
improving the quality of knowledge. Undesirable situations such as confident
incorrect and unconfident correct knowledge prevent learners from revising
their knowledge because it is not always easy for them to perceive the
situations. To solve this problem, we propose a system that estimates
self-confidence while solving multiple-choice questions by eye tracking and
gives feedback about which question should be reviewed carefully. We report the
results of three studies measuring its effectiveness. (1) On a well-controlled
dataset with 10 participants, our approach detected confidence and unconfidence
with 81% and 79% average precision. (2) With the help of 20 participants, we
observed that correct answer rates of questions were increased by 14% and 17%
by giving feedback about correct answers without confidence and incorrect
answers with confidence, respectively. (3) We conducted a large-scale data
recording in a private school (72 high school students solved 14,302 questions)
to investigate effective features and the number of required training samples.
