Self-confidence plays 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. Therefore we propose a system that estimates self-confidence by sensors and gives feedback about which question should be reviewed carefully.
Although every learner has different preferences in reading, textbooks have traditionally been static. Reading experiences should become more immersive and interesting if textbooks behave differently for each learner. We propose vivid interactions optimized for the context of learning by combining a digital document and affective state recognition (e.g. interest, mental workload, and self-confidence) using smart sensors.
We propose a system tracking the number of read words by analyzing eye movements measured by JINS MEME (commercial electrooculography glasses). As people are encouraged to be physically fit by monitoring step counts, counting the number of words they read and giving feedback is a potential approach to helping them increase their daily reading volume.
Mental illness, especially depression is one of the most pressing concerns all over the world. We propose a system estimating mental states of a user from activity log derived from sensors. It uses the analogy of thermometer for the visualization. Everyone should have gone through hardships with fever, and they can understand how much tired by this format.