Zhang Bingxue, Chai Chengliang, Yin Zhong, Shi Yang
Department of Optical-Electrical & Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Brain Sci. 2021 May 11;11(5):613. doi: 10.3390/brainsci11050613.
Existing methods for learning-style recognition are highly subjective and difficult to implement. Therefore, the present study aimed to develop a learning-style recognition mechanism based on EEG features. The process for the mechanism included labeling learners' actual learning styles, designing a method to effectively stimulate different learners' internal state differences regarding learning styles, designing the data-collection method, designing the preprocessing procedure, and constructing the recognition model. In this way, we designed and verified an experimental method that can effectively stimulate learning-style differences in the information-processing dimension. In addition, we verified the effectiveness of using EEG signals to recognize learning style. The recognition accuracy of the learning-style processing dimension was 71.2%. This result is highly significant for the further exploration of using EEG signals for effective learning-style recognition.
现有的学习风格识别方法主观性很强且难以实施。因此，本研究旨在开发一种基于脑电图（EEG）特征的学习风格识别机制。该机制的过程包括标记学习者的实际学习风格、设计一种有效激发不同学习者在学习风格方面内部状态差异的方法、设计数据收集方法、设计预处理程序以及构建识别模型。通过这种方式，我们设计并验证了一种能够有效激发信息处理维度上学习风格差异的实验方法。此外，我们验证了使用脑电信号识别学习风格的有效性。学习风格处理维度的识别准确率为71.2%。这一结果对于进一步探索利用脑电信号进行有效的学习风格识别具有重要意义。
