Department of Management Information Systems, National Pingtung University of Science & Technology, Neipu, Pingtung 912, Taiwan.
Sensors (Basel). 2013 Aug 9;13(8):10273-86. doi: 10.3390/s130810273.
During the learning process, whether students remain attentive throughout instruction generally influences their learning efficacy. If teachers can instantly identify whether students are attentive they can be suitably reminded to remain focused, thereby improving their learning effects. Traditional teaching methods generally require that teachers observe students' expressions to determine whether they are attentively learning. However, this method is often inaccurate and increases the burden on teachers. With the development of electroencephalography (EEG) detection tools, mobile brainwave sensors have become mature and affordable equipment. Therefore, in this study, whether students are attentive or inattentive during instruction is determined by observing their EEG signals. Because distinguishing between attentiveness and inattentiveness is challenging, two scenarios were developed for this study to measure the subjects' EEG signals when attentive and inattentive. After collecting EEG data using mobile sensors, various common features were extracted from the raw data. A support vector machine (SVM) classifier was used to calculate and analyze these features to identify the combination of features that best indicates whether students are attentive. Based on the experiment results, the method proposed in this study provides a classification accuracy of up to 76.82%. The study results can be used as a reference for learning system designs in the future.
在学习过程中，学生是否能全程保持专注通常会影响其学习效果。如果教师能够即时识别出学生是否专注，就可以适当地提醒他们保持专注，从而提高他们的学习效果。传统的教学方法通常要求教师观察学生的表情，以确定他们是否在专心学习。然而，这种方法往往不够准确，还增加了教师的负担。随着脑电图（EEG）检测工具的发展，移动脑波传感器已成为成熟且价格合理的设备。因此，在这项研究中，通过观察学生的脑电图信号来判断他们在教学过程中是否专注。由于区分专注和不专注具有一定的挑战性，本研究设计了两种场景来测量被试在专注和不专注时的脑电图信号。使用移动传感器收集 EEG 数据后，从原始数据中提取各种常见特征。使用支持向量机（SVM）分类器对这些特征进行计算和分析，以确定最能表明学生是否专注的特征组合。基于实验结果，本研究提出的方法提供了高达 76.82%的分类准确率。该研究结果可作为未来学习系统设计的参考。
