This paper presents a novel approach to mind-wandering prediction in the context of webcam-based online learning. We implemented a Singular Value Decomposition (SVD)-based 1D temporal eye-signal extraction method, which relies solely on eye landmark detection and eliminates the need for gaze tracking or specialized hardware, then extract suitable features from the signals to train the prediction model. Our thorough experimental framework facilitates the evaluation of our approach alongside baseline models, particularly in the analysis of temporal eye signals and the prediction of attentional states. Notably, our SVD-based signal captures both subtle and major eye movements, including changes in the eye boundary and pupil, surpassing the limited capabilities of eye aspect ratio (EAR)-based signals. Our proposed model exhibits a 2% improvement in the overall Area Under the Receiver Operating Characteristics curve (AUROC) metric and 7% in the F1-score metric for ‘not-focus’ prediction, compared to the combination of EAR-based and computationally intensive gaze-based models used in the baseline study These contributions have potential implications for enhancing the field of attentional state prediction in online learning, offering a practical and effective solution to benefit educational experiences.

Eye blinking detection in the wild plays an essential role in deception detection, driving fatigue detection, etc. Despite the fact that numerous attempts have already been made, the majority of them have encountered difficulties, such as the derived eye images having different resolutions as the distance between the face and the camera changes; or the requirement of a lightweight detection model to obtain a short inference time in order to perform in real-time. In this research, two problems are addressed: how the eye blinking detection model can learn efficiently from different resolutions of eye pictures in diverse conditions; and how to reduce the size of the detection model for faster inference time. We propose to utilize upsampling and downsampling the input eye images to the same resolution as one potential solution for the first problem, then find out which interpolation method can result in the highest performance of the detection model. For the second problem, although a recent spatiotemporal convolutional neural network used for eye blinking detection has a strong capacity to extract both spatial and temporal characteristics, it remains having a high number of network parameters, leading to high inference time. Therefore, using Depth-wise Separable Convolution rather than conventional convolution layers inside each branch is considered in this paper as a feasible solution.

Online education is an inevitable trend. With just a smartphone, a tablet, or a personal computer, anyone, from teenagers to middle-aged individuals, from schoolchildren to workers, can access any free or paid courses in every aspect of society from economics, politics, literature, history, etc. foreign languages; Learners are free to choose lecturers, duration, time, space, and learning concepts as long as it suits their needs. However, online education still harbors many limitations, especially the issue of student distraction during online learning. This article elucidates the impact of online learning on student focus and analyzes the necessity of an attention detection system when studying online. Through surveys, the results indicated that up to 82.57% of students frequently lose focus when engaging in online learning, and 100% of opinions from education experts concur on the limitations of online learning. Based on these analyses, we provide insights into the necessity of developing and the crucial characteristics of an attention detection system to support learners in online education.