{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T13:58:50Z","timestamp":1774447130491,"version":"3.50.1"},"reference-count":47,"publisher":"Association for Computing Machinery (ACM)","issue":"3","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62273219 and 62276171"],"award-info":[{"award-number":["62273219 and 62276171"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Program for Innovative Research Team in Universities of Inner Mongolia Autonomous Region","award":["NMGIRT2317"],"award-info":[{"award-number":["NMGIRT2317"]}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2024A15 15011938"],"award-info":[{"award-number":["2024A15 15011938"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shenzhen Fundamental Research Project","award":["ZDCY20250901110940006"],"award-info":[{"award-number":["ZDCY20250901110940006"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>\n                    Knowledge tracing aims to dynamically track and assess learners\u2019 mastery of specific knowledge concepts (e.g., item taxonomies, skill hierarchies). Integrating it into recommender systems greatly enhances model explainability, yet current models either overemphasize temporal dynamics or overlook inter-concept spatial correlations, resulting in suboptimal performance in integrated spatiotemporal modeling. Such disjointed designs also lead to limited adaptability in long learning sequences, poor compatibility with real-world dynamic scenarios, and restricted model interpretability. To solve these problems, we propose an A-SGNN framework fusing higher-order path spiking and graph neural networks, which leverages Graph Convolutional Network (GCN) and Spiking Neural Network (SNN) modules to make up for existing shortcomings. Specifically, a Bidirectional GCN module fully captures bidirectional high-order spatial graph structures between concepts, accurately modeling multi-knowledge relationships and reducing information loss. An SNN module with adaptive path-finding strategy dynamically optimizes individual learning trajectories, overcoming traditional fixed-path rigidity, while its membrane potential decay simulates human forgetting. A time decay factor is integrated to better capture memory effects in learning. Experiments demonstrate that our method outperforms state-of-the-art approaches in predicting learners\u2019 future academic performance, with this advantage deriving from the combined synergistic benefits of the GCN and SNN modules. Our implementation is publicly available at:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/jianruichen\/A-SGNN\">https:\/\/github.com\/jianruichen\/A-SGNN<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3798162","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T14:06:50Z","timestamp":1771855610000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Higher-Order Spiking and Graph Neural Network for Knowledge Tracing"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2780-9826","authenticated-orcid":false,"given":"Jinru","family":"Hu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Shaanxi Normal University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9104-4540","authenticated-orcid":false,"given":"Jianrui","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, and Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9355-5743","authenticated-orcid":false,"given":"Yuxuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Shaanxi Normal University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1223-6996","authenticated-orcid":false,"given":"Hao","family":"Liao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3206447"},{"issue":"11","key":"e_1_3_2_3_2","first-page":"224:1","article-title":"Knowledge tracing: A survey","volume":"55","author":"Abdelrahman Ghodai","year":"2023","unstructured":"Ghodai Abdelrahman, Qing Wang, and Bernardo Nunes. 2023. Knowledge tracing: A survey. Computing Surveys 55, 11 (2023), 224:1\u2013224:37.","journal-title":"Computing Surveys"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-021-10562-2"},{"issue":"3","key":"e_1_3_2_5_2","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1038\/s41567-024-02757-w","article-title":"Topology shapes dynamics of higher-order networks","volume":"21","author":"Millan A.","year":"2025","unstructured":"A. Millan, H. Sun, L. Giambagli, R. Muolo, T. Carletti, J. Torres, F. Radicchi, J. Kurths, and G. Bianconi. 2025. Topology shapes dynamics of higher-order networks. Nature Physics 21, 3, Article 2485 (2025), 353\u2013361.","journal-title":"Nature Physics"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2024.102427"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i1.19874"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557622"},{"key":"e_1_3_2_9_2","doi-asserted-by":"crossref","first-page":"5027","DOI":"10.1609\/aaai.v37i4.25630","article-title":"Set-to-sequence ranking-based concept-aware learning path recommendation","volume":"37","author":"Chen Xianyu","year":"2023","unstructured":"Xianyu Chen, Jian Shen, Wei Xia, Jiarui Jin, Yakun Song, Weinan Zhang, Weiwen Liu, Menghui Zhu, Ruiming Tang, Kai Dong, et al. 2023. Set-to-sequence ranking-based concept-aware learning path recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, 5027\u20135035.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"3","key":"e_1_3_2_10_2","first-page":"78:1","article-title":"DGEKT: A dual graph ensemble learning method for knowledge tracing","volume":"42","author":"Cui Chaoran","year":"2024","unstructured":"Chaoran Cui, Yumo Yao, Chunyun Zhang, Hebo Ma, Yuling Ma, Zhaochun Ren, Chen Zhang, and James Ko. 2024. DGEKT: A dual graph ensemble learning method for knowledge tracing. ACM Transactions on Information Systems 42, 3 (2024), 78:1\u201378:24.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3263008"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.09.005"},{"key":"e_1_3_2_13_2","doi-asserted-by":"crossref","unstructured":"Jianhao Ding Zhaofei Yu Yonghong Tian and Tiejun Huang. 2021. Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks. arXiv:2105.11654. Retrieved from https:\/\/arxiv.org\/abs\/2105.11654","DOI":"10.24963\/ijcai.2021\/321"},{"key":"e_1_3_2_14_2","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/B978-0-12-816176-0.00026-0","volume-title":"Handbook of Medical Image Computing and Computer Assisted Intervention","author":"DiPietro Robert","year":"2020","unstructured":"Robert DiPietro and Gregory D. Hager. 2020. Deep learning: RNNs and LSTM. In Handbook of Medical Image Computing and Computer Assisted Intervention. S. Kevin Zhou, Daniel Rueckert, and Gabor Fichtinger (Eds.). Elsevier, 503\u2013519."},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00266"},{"issue":"6","key":"e_1_3_2_16_2","doi-asserted-by":"crossref","first-page":"5347","DOI":"10.1109\/TVT.2021.3077691","article-title":"Multi-path and multi-hop task offloading in mobile ad hoc networks","volume":"70","author":"Feng Guangsheng","year":"2021","unstructured":"Guangsheng Feng, Xin Li, Zihan Gao, Chengbo Wang, Hongwu Lv, and Qian Zhao. 2021. Multi-path and multi-hop task offloading in mobile ad hoc networks. IEEE Transactions on Vehicular Technology 70, 6 (2021), 5347\u20135361.","journal-title":"IEEE Transactions on Vehicular Technology"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462932"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403282"},{"key":"e_1_3_2_19_2","first-page":"388","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Han Bing","year":"2020","unstructured":"Bing Han and Kaushik Roy. 2020. Deep spiking neural network: Energy efficiency through time-based coding. In Proceedings of the European Conference on Computer Vision, 388\u2013404."},{"key":"e_1_3_2_20_2","doi-asserted-by":"crossref","first-page":"2355","DOI":"10.1007\/978-1-0716-1006-0_795","volume-title":"Encyclopedia of Computational Neuroscience","author":"Hines Michael","year":"2022","unstructured":"Michael Hines, Ted Carnevale, and Robert A. McDougal. 2022. NEURON simulation environment. In Encyclopedia of Computational Neuroscience. Dieter Jaeger and Ranu Jung (Eds.). Springer, 2355\u20132361."},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3379507"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2022.11.016"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111300"},{"key":"e_1_3_2_24_2","first-page":"15251","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Kim Sungjune","year":"2024","unstructured":"Sungjune Kim, Hyung-Gun Chi, Hyerin Lim, Karthik Ramani, Jinkyu Kim, and Sangpil Kim. 2024. Higher-order relational reasoning for pedestrian trajectory prediction. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 15251\u201315260."},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2021.103466"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122404"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-63031-6_15"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2021.3083177"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.02.044"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462827"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42005-022-00858-7"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3649436"},{"issue":"7","key":"e_1_3_2_33_2","doi-asserted-by":"crossref","first-page":"e0120644","DOI":"10.1371\/journal.pone.0120644","article-title":"Replication and analysis of Ebbinghaus\u2019 forgetting curve","volume":"10","year":"2015","unstructured":"Jaap M. J. Murre and Joeri Dros. 2015. Replication and analysis of Ebbinghaus\u2019 forgetting curve. PloS One 10, 7 (2015), e0120644.","journal-title":"PloS One"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3179968"},{"key":"e_1_3_2_35_2","first-page":"384","volume-title":"Proceedings of the 12th International Conference on Educational Data Mining","author":"Pandey Shalini","year":"2019","unstructured":"Shalini Pandey and George Karypis. 2019. A self-attentive model for knowledge tracing. In Proceedings of the 12th International Conference on Educational Data Mining, 384\u2013389."},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411994"},{"key":"e_1_3_2_37_2","first-page":"505","volume-title":"Advances in Neural Information Processing Systems","volume":"28","author":"Piech Chris","year":"2015","unstructured":"Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J. Guibas, and Jascha Sohl-Dickstein. 2015. Deep knowledge tracing. In Advances in Neural Information Processing Systems, Vol. 28, 505\u2013513."},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/TLT.2024.3383325"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110036"},{"key":"e_1_3_2_40_2","first-page":"287","volume-title":"Proceedings of the 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery","author":"Wang Wei","year":"2020","unstructured":"Wei Wang, Tieyuan Liu, Liang Chang, Tianlong Gu, and Xuemei Zhao. 2020. Convolutional recurrent neural networks for knowledge tracing. In Proceedings of the 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, 287\u2013290."},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03621-1"},{"key":"e_1_3_2_42_2","first-page":"2501","volume-title":"Proceedings of the 31st International Joint Conference on Artificial Intelligence","author":"Wang Yuchen","year":"2022","unstructured":"Yuchen Wang, Malu Zhang, Yi Chen, and Hong Qu. 2022. Signed neuron with memory: Towards simple, accurate and high-efficient ANN-SNN conversion. In Proceedings of the 31st International Joint Conference on Artificial Intelligence, 2501\u20132508."},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.112958"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583255"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052580"},{"issue":"194","key":"e_1_3_2_46_2","first-page":"1","article-title":"On the intrinsic structures of spiking neural networks","volume":"25","author":"Zhang Shao-Qun","year":"2024","unstructured":"Shao-Qun Zhang, Jia-Yi Chen, Jin-Hui Wu, Gao Zhang, Huan Xiong, Bin Gu, and Zhi-Hua Zhou. 2024. On the intrinsic structures of spiking neural networks. Journal of Machine Learning Research 25, 194 (2024), 1\u201374.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_47_2","first-page":"19345","volume-title":"Advances in Neural Information Processing Systems","volume":"35","author":"Zhang Shao-Qun","year":"2022","unstructured":"Shao-Qun Zhang and Zhi-Hua Zhou. 2022. Theoretically provable spiking neural networks. In Advances in Neural Information Processing Systems, Vol. 35, 19345\u201319356."},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/338"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3798162","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T12:14:15Z","timestamp":1774440855000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3798162"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,25]]},"references-count":47,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3,31]]}},"alternative-id":["10.1145\/3798162"],"URL":"https:\/\/doi.org\/10.1145\/3798162","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,25]]},"assertion":[{"value":"2025-05-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-02-12","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}