Abstract
Diagnosing the contents of learning in brain activities is a long-standing research task in cognitive sciences. The current studies on cognitive diagnosis (CD) in education determine the status of knowledge concept (KC) based on the observed responses to test items. However, the learning process of KC in the brain is left with no touch. This paper proposes to solve the problem of knowledge-concept diagnosis (KCD) from fMRIs by identifying the concepts a student focuses on in learning activities. Using the graph convolutional network (GCN), we introduce the STEGCN approach composed of a spatial GCN for brain-graph structure, a temporal GCN for brain-activity sequence, and a fully connected network for KCD. To evaluate STEGCN, we acquired an fMRI dataset that was collected on five concepts when students were learning a computer course. The experiment results demonstrate that our proposed method yields better performance than traditional models, showing the effectiveness of STEGCN in concept classification. This study contributes to a new fMRI-based route for knowledge-concept diagnosis.
This study was funded in part by the National Natural Science Foundation of China (Nos. 62272392, U1811262, 61802313), the Key Research and Development Program of China (No. 2020AAA0108504), the Higher Research Funding on International Talent cultivation at Northwestern Polytechnical University (No. GJGZZD202202).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zeithamova, D., et al.: Brain mechanisms of concept learning. J. Neurosci. 39(42), 8259–8266 (2019)
Zhang, Y., Dai, H., Yun, Y., Liu, S., Lan, A., Shang, X.: Meta-knowledge dictionary learning on 1-bit response data for student knowledge diagnosis. Knowl.-Based Syst. 205, 106290 (2020)
Pliakos, K., et al.: Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems. Comput. Educ. 137, 91–103 (2019)
Piech, C., et al.: Deep knowledge tracing. Adv. Neural Inf. Process. Syst. 28 (2015)
Yun, Y., Dai, H., Cao, R., Zhang, Y., Shang, X.: Self-paced graph memory network for student GPA prediction and abnormal student detection. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds.) AIED 2021. LNCS (LNAI), vol. 12749, pp. 417–421. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78270-2_74
Li, S., Xing, X., Liu, Y., Yang, Z., Niu, Y., Jia, Z.: Multi-preference book recommendation method based on graph convolution neural network. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds.) Web Information Systems and Applications. WISA 2022. LNCS, vol. 13579, pp. 521–532. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20309-1_46
Chen, X., Sun, Y., Zhou, T., Wen, Y., Zhang, F., Zeng, Q.: Recommending online course resources based on knowledge graph. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds.) Web Information Systems and Applications. WISA 2022. LNCS, vol. 13579, pp. 581–588. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20309-1_51
Steiner, A.R., Rousseau-Blass, F., Schroeter, A., Hartnack, S., Bettschart-Wolfensberger, R.: Systematic review: anesthetic protocols and management as confounders in rodent blood oxygen level dependent functional magnetic resonance imaging (BOLD fMRI). Animals 11(1) (2021)
Zhang, Y., Xu, Y., An, R., Li, Y., Liu, S., Shang, X.: Markov guided spatio-temporal networks for brain image classification. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2035–2041. IEEE (2022)
Lynn, C.W., Bassett, D.S.: The physics of brain network structure, function and control. Nat. Rev. Phys. 1(5), 318–332 (2019)
Enge, A., Friederici, A.D., Skeide, M.A.: A meta-analysis of fMRI studies of language comprehension in children. NeuroImage 215, 116858 (2020)
Beaty, R.E., et al.: Robust prediction of individual creative ability from brain functional connectivity. Proc. Natl. Acad. Sci. 115(5), 1087–1092 (2018)
Santana, A.N., Cifre, I., De Santana, C.N., Montoya, P.: Using deep learning and resting-state fMRI to classify chronic pain conditions. Front. Neurosci. 13, 1313 (2019)
Rolls, E.T., Huang, C.C., Lin, C.P., Feng, J., Joliot, M.: Automated anatomical labelling atlas 3. Neuroimage 206, 116189 (2020)
Craddock, R.C., James, G.A., Holtzheimer III, P.E., Hu, X.P., Mayberg, H.S.: A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 33(8), 1914–1928 (2012)
Bessadok, A., Mahjoub, M.A., Rekik, I.: Graph neural networks in network neuroscience. IEEE Trans. Pattern Anal. Mach. Intell. (2022)
Li, X., et al.: Interpretable brain graph neural network for fMRI analysis. Med. Image Anal. 74, 102233 (2021)
Meshulam, M., et al.: Think like an expert: neural alignment predicts understanding in students taking an introduction to computer science course. bioRxiv, pp. 2020–05 (2020)
Feng, W., Liu, G., Zeng, K., Zeng, M., Liu, Y.: A review of methods for classification and recognition of ASD using fMRI data. J. Neurosci. Methods 368, 109456 (2022)
Yin, W., Li, L., Fang-Xiang, W.: Deep learning for brain disorder diagnosis based on fMRI images. Neurocomputing 469, 332–345 (2022)
Qureshi, M.N.I., Jooyoung, O., Lee, B.: 3D-CNN based discrimination of schizophrenia using resting-state fMRI. Artif. Intell. Med. 98, 10–17 (2019)
Zhang, L., Wang, M., Liu, M., Zhang, D.: A survey on deep learning for neuroimaging-based brain disorder analysis. Front. Neurosci. 14, 779 (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Hammond, D.K., Vandergheynst, P., Gribonval, R.: Wavelets on graphs via spectral graph theory. Appl. Comput. Harmon. Anal. 30(2), 129–150 (2011)
Zhang, Y., Xiang, M., Yang, B.: Low-rank preserving embedding. Pattern Recognit. 70, 112–125 (2017)
Zhang, Y., An, R., Liu, S., Cui, J., Shang, X.: Predicting and understanding student learning performance using multi-source sparse attention convolutional neural networks. IEEE Trans. Big Data (2021)
Zhang, Y., Liu, S., Qu, X., Shang, X.: Multi-instance discriminative contrastive learning for brain image representation. Neural Comput. Appl. 1–14 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lei, Y., Zhang, Y., Lin, Y., Shang, X. (2023). Knowledge-Concept Diagnosis from fMRIs by Using a Space-Time Embedding Graph Convolutional Network. In: Yuan, L., Yang, S., Li, R., Kanoulas, E., Zhao, X. (eds) Web Information Systems and Applications. WISA 2023. Lecture Notes in Computer Science, vol 14094. Springer, Singapore. https://doi.org/10.1007/978-981-99-6222-8_9
Download citation
DOI: https://doi.org/10.1007/978-981-99-6222-8_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-6221-1
Online ISBN: 978-981-99-6222-8
eBook Packages: Computer ScienceComputer Science (R0)
