Self-supervised Deep Learning for Reading Activity Classification

TL;DR

This paper introduces a self-supervised deep learning approach for reading activity classification that outperforms traditional supervised methods, especially with limited labeled data, across reading detection and confidence estimation tasks.

Contribution

The paper presents a novel self-supervised deep learning method for reading analysis, demonstrating its effectiveness over supervised models in two classification tasks.

Findings

Self-supervised DL outperforms supervised DL and SVMs in reading detection.

The method is especially effective with scarce training data.

Results inform the design of automatic reading analysis platforms.

Abstract

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.