Deeply sensing learners for better assistance: Towards distribution of learning experiences

Abstract

Most of the current e-learning systems rely on shallow sensing of learners such as achievement tests and log of usage of e-learning systems. This poses a limitation to know internal states of learners such as confidence and the level of knowledge. To solve this problem, we propose to employ deeper sensing by using eye trackers, EOG, EEG, motion and physiological sensors. As tasks, we consider English learning. The sensing technologies described in this paper includes low level estimations (the number of read words, the period of reading), document type recognition and identification of read words, as well as high level estimations about confidence of answers, the English ability in terms of TOEIC scores and unknown words encountered while reading English documents. Such functionality helps learners and teachers to know the internal states and will be used to describe learning experiences to be shared by other learners.