Abstract
Studying brain activity during online learning will help to improve research on brain function based on real online learning situations, and will also promote the scientific evaluation of online education. Existing research focuses on enhancing learning effects and evaluating the learning process associated with online learning from an attentional perspective. We aimed to comparatively analyze the differences in prefrontal cortex (PFC) activity during resting, studying, and question-answering states in online learning and to establish a classification model of the learning state that would be useful for the evaluation of online learning. Nineteen university students performed experiments using functional near-infrared spectroscopy (fNIRS) to monitor the prefrontal lobes. The resting time at the start of the experiment was the resting state, watching 13 videos was the learning state, and answering questions after the video was the answering state. Differences in student activity between these three states were analyzed using a general linear model, 1s fNIRS data clips, and features, including averages from the three states, were classified using machine learning classification models such as support vector machines and k-nearest neighbor. The results show that the resting state is more active than learning in the dorsolateral prefrontal cortex, while answering questions is the most active of the three states in the entire PFC, and k-nearest neighbor achieves 98.5% classification accuracy for 1s fNIRS data. The results clarify the differences in PFC activity between resting, learning, and question-answering states in online learning scenarios and support the feasibility of developing an online learning assessment system using fNIRS and machine learning techniques.