Zhang Wenxin, Zhang Yiping, Sun Liqian, Zhang Yupei, Shang Xuequn
School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
Big Data Storage and Management MIIT Lab, Xi'an, China.
Front Neurosci. 2025 Mar 21;19:1499629. doi: 10.3389/fnins.2025.1499629. eCollection 2025.
Knowledge concept recognition (KCR) aims to identify the concepts learned in the brain, which has been a longstanding area of interest for learning science and education. While many studies have investigated object recognition using brain fMRIs, there are limited research on identifying specific knowledge points within the classroom. In this paper, we propose to recognize the knowledge concepts in computer science by classifying the brain fMRIs taken when students are learning the concepts. More specifically, this study made attempts on two representation strategies, i.e., voxel and time difference. Based on the representations, we evaluated traditional classifiers and the combination of CNN and LSTM for KCR. Experiments are conducted on a public dataset collected from 25 students and teachers in a computer science course. The evaluations of classifying fMRI segments show that the used classifiers all can attain a good performance when using the time-difference representation, where the CNN-LSTM model reaches the highest accuracy. This research contributes to the understanding of human learning and supports the development of personalized learning.
知识概念识别（KCR）旨在识别大脑中所学的概念，这一直是学习科学和教育领域长期关注的一个领域。虽然许多研究已经使用脑功能磁共振成像（fMRI）来研究物体识别，但在识别课堂内的特定知识点方面的研究却很有限。在本文中，我们建议通过对学生学习概念时所拍摄的脑fMRI进行分类，来识别计算机科学中的知识概念。更具体地说，本研究尝试了两种表示策略，即体素和时间差。基于这些表示，我们评估了传统分类器以及用于KCR的CNN和LSTM的组合。实验是在一个从计算机科学课程的25名学生和教师那里收集的公共数据集上进行的。对fMRI片段分类的评估表明，当使用时间差表示时，所使用的分类器都能取得良好的性能，其中CNN-LSTM模型达到了最高的准确率。这项研究有助于对人类学习的理解，并支持个性化学习的发展。
