Self-supervised Deep Learning for Reading Activity Classification
This research is significant for developers of automatic reading analysis platforms, as it provides a method that performs well even with limited labeled data, which is a common problem in this domain.
This paper addresses the challenge of limited labeled data in automatic reading analysis by proposing a self-supervised deep learning method. The method was evaluated on reading detection using EOG data and confidence estimation using eye-tracking data, outperforming fully-supervised deep learning and SVMs, particularly with scarce training data.
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.