Knowledge concept recognition in the learning brain via fMRI classification
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 kno...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2025.1499629/full |
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| author | Wenxin Zhang Wenxin Zhang Yiping Zhang Liqian Sun Yupei Zhang Yupei Zhang Xuequn Shang Xuequn Shang |
| author_facet | Wenxin Zhang Wenxin Zhang Yiping Zhang Liqian Sun Yupei Zhang Yupei Zhang Xuequn Shang Xuequn Shang |
| author_sort | Wenxin Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7bd9c38bc6bc4817b46f103f3c7df7a8 |
| institution | DOAJ |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-7bd9c38bc6bc4817b46f103f3c7df7a82025-08-20T02:51:00ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-03-011910.3389/fnins.2025.14996291499629Knowledge concept recognition in the learning brain via fMRI classificationWenxin Zhang0Wenxin Zhang1Yiping Zhang2Liqian Sun3Yupei Zhang4Yupei Zhang5Xuequn Shang6Xuequn Shang7School of Computer Science, Northwestern Polytechnical University, Xi'an, ChinaBig Data Storage and Management MIIT Lab, Xi'an, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi'an, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi'an, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi'an, ChinaBig Data Storage and Management MIIT Lab, Xi'an, ChinaSchool of Computer Science, Northwestern Polytechnical University, Xi'an, ChinaBig Data Storage and Management MIIT Lab, Xi'an, ChinaKnowledge 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.https://www.frontiersin.org/articles/10.3389/fnins.2025.1499629/fullknowledge concept recognitiondeep learningfMRI classificationbrain identificationlearning science |
| spellingShingle | Wenxin Zhang Wenxin Zhang Yiping Zhang Liqian Sun Yupei Zhang Yupei Zhang Xuequn Shang Xuequn Shang Knowledge concept recognition in the learning brain via fMRI classification Frontiers in Neuroscience knowledge concept recognition deep learning fMRI classification brain identification learning science |
| title | Knowledge concept recognition in the learning brain via fMRI classification |
| title_full | Knowledge concept recognition in the learning brain via fMRI classification |
| title_fullStr | Knowledge concept recognition in the learning brain via fMRI classification |
| title_full_unstemmed | Knowledge concept recognition in the learning brain via fMRI classification |
| title_short | Knowledge concept recognition in the learning brain via fMRI classification |
| title_sort | knowledge concept recognition in the learning brain via fmri classification |
| topic | knowledge concept recognition deep learning fMRI classification brain identification learning science |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2025.1499629/full |
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