  Reading analysis can give important information about a user's confidence and
habits and can be used to construct feedback to improve a user's reading
behavior. A lack of labeled data inhibits the effective application of
fully-supervised Deep Learning (DL) for automatic reading analysis. In this
paper, we propose a self-supervised DL method for reading analysis and evaluate
it on two classification tasks. We first evaluate the proposed self-supervised
DL method on a four-class classification task on reading detection using
electrooculography (EOG) glasses datasets, followed by an evaluation of a
two-class classification task of confidence estimation on answers of
multiple-choice questions (MCQs) using eye-tracking datasets. Fully-supervised
DL and support vector machines (SVMs) are used to compare the performance of
the proposed self-supervised DL method. The results show that the proposed
self-supervised DL method is superior to the fully-supervised DL and SVM for
both tasks, especially when training data is scarce. This result indicates that
the proposed self-supervised DL method is the superior choice for reading
analysis tasks. The results of this study are important for informing the
design and implementation of automatic reading analysis platforms.
