Automated classification of EEG signals for predicting students' cognitive state during learning

Xi Liu, P. Tan, Lei Liu, S. Simske
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引用次数: 16

Abstract

For distance learning applications, inferring the cognitive states of students, particularly, their concentration and comprehension levels during instruction, is important to assess their learning efficacy. In this paper, we investigated the feasibility of using EEG recordings generated from an off-the-shelf, wearable device to automatically classify the cognitive states of students as they were asked to perform a series of reading and question answering tasks. We showed that the EEG data can effectively predict whether a student is attentive or distracted as well as the student's reading speed, which is an important measure of reading fluency. However, the EEG signals alone are insufficient to predict how well the students can correctly answer questions related to the reading materials as there were other confounding factors, such as the students' background knowledge, that must be taken into consideration. We also showed that the accuracy in predicting the different cognitive states depends on the choice of classifier used (global, local, or multi-task learning). For example, the concentration level of a student can be accurately predicted using a local model whereas a global model that incorporates side information about the student's background knowledge is more effective at predicting whether the student will correctly answer questions about the materials they read.
脑电信号自动分类预测学生学习过程中的认知状态
在远程学习应用中,推断学生的认知状态,特别是他们在教学中的注意力和理解水平,对于评估他们的学习效果是重要的。在本文中,我们研究了使用现成的可穿戴设备生成的脑电图记录来自动分类学生在执行一系列阅读和问答任务时的认知状态的可行性。我们发现脑电图数据可以有效地预测学生的注意力是否集中,以及学生的阅读速度,这是阅读流畅性的重要衡量标准。然而,仅凭脑电图信号不足以预测学生正确回答阅读材料相关问题的能力,因为还需要考虑学生的背景知识等其他干扰因素。我们还表明,预测不同认知状态的准确性取决于所使用分类器的选择(全局、局部或多任务学习)。例如,使用局部模型可以准确地预测学生的集中程度,而使用包含学生背景知识的侧面信息的全局模型则可以更有效地预测学生是否会正确回答有关他们所阅读材料的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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