{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T03:34:35Z","timestamp":1771990475318,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2024,4,13]],"date-time":"2024-04-13T00:00:00Z","timestamp":1712966400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,13]],"date-time":"2024-04-13T00:00:00Z","timestamp":1712966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Key R&D Program of Zhejiang Province","award":["No. 2024C03262"],"award-info":[{"award-number":["No. 2024C03262"]}]},{"name":"the Key R&D Program of Zhejiang Province","award":["No. 2024C03262"],"award-info":[{"award-number":["No. 2024C03262"]}]},{"DOI":"10.13039\/501100004731","name":"Zhejiang Provincial Natural Science Foundation","doi-asserted-by":"crossref","award":["No. LY22F020004"],"award-info":[{"award-number":["No. LY22F020004"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004731","name":"Zhejiang Provincial Natural Science Foundation","doi-asserted-by":"crossref","award":["No. LY22F020004"],"award-info":[{"award-number":["No. LY22F020004"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s13042-024-02124-4","type":"journal-article","created":{"date-parts":[[2024,4,13]],"date-time":"2024-04-13T10:01:46Z","timestamp":1713002506000},"page":"3863-3877","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Framelet-based dual hypergraph neural networks for student performance prediction"],"prefix":"10.1007","volume":"15","author":[{"given":"Yazhi","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiandong","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamido","family":"Fujita","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,13]]},"reference":[{"key":"2124_CR1","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1007\/s13042-015-0341-x","volume":"8","author":"N Iam-On","year":"2017","unstructured":"Iam-On N, Boongoen T (2017) Improved student dropout prediction in Thai University using ensemble of mixed-type data clusterings. Int J Mach Learn Cybern 8:497\u2013510","journal-title":"Int J Mach Learn Cybern"},{"issue":"1","key":"2124_CR2","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s13042-021-01348-y","volume":"13","author":"MS Kiran","year":"2022","unstructured":"Kiran MS, Siramkaya E, Esme E, Senkaya MN (2022) Prediction of the number of students taking make-up examinations using artificial neural networks. Int J Mach Learn Cybern 13(1):71\u201381","journal-title":"Int J Mach Learn Cybern"},{"key":"2124_CR3","doi-asserted-by":"publisher","first-page":"2913","DOI":"10.1007\/s13042-023-01809-6","volume":"14","author":"W Huang","year":"2023","unstructured":"Huang W, Xiao T, Liu Q, Huang Z, Ma J, Chen E (2023) HMNet: a hierarchical multi-modal network for educational video concept prediction. Int J Mach Learn Cybern 14:2913\u20132924","journal-title":"Int J Mach Learn Cybern"},{"key":"2124_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2019.103676","volume":"143","author":"N Tomasevic","year":"2020","unstructured":"Tomasevic N, Gvozdenovic N, Vranes S (2020) An overview and comparison of supervised data mining techniques for student exam performance prediction. Comput Educ 143:103676","journal-title":"Comput Educ"},{"key":"2124_CR5","unstructured":"Lin Y, Chen H, Xia W, Lin F, Wu P, Wang Z, Li Y (2023) A comprehensive survey on deep learning techniques in educational data mining. arXiv preprint arXiv:2309.04761"},{"key":"2124_CR6","unstructured":"Hu Q, Rangwala H (2019) Academic performance estimation with attention-based graph convolutional networks. In: Proceedings of the 12th International Conference on Educational Data Mining (EDM), pp 69\u201378"},{"key":"2124_CR7","unstructured":"Karimi H, Derr T, Huang J, Tang J (2020) Online academic course performance prediction using relational graph convolutional neural network. In: Proceedings of the 13th International Conference on Educational Data Mining, pp 444\u2013450"},{"key":"2124_CR8","doi-asserted-by":"crossref","unstructured":"Li H, Wei H, Wang Y, Song Y, Qu H (2020) Peer-inspired student performance prediction in interactive online question pools with graph neural network. In: Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM), pp 2589\u20132596","DOI":"10.1145\/3340531.3412733"},{"key":"2124_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105174","volume":"114","author":"M Li","year":"2022","unstructured":"Li M, Zhang Y, Li X, Cai L, Yin B (2022) Multi-view hypergraph neural networks for student academic performance prediction. Eng Appl Artif Intell 114:105174","journal-title":"Eng Appl Artif Intell"},{"key":"2124_CR10","doi-asserted-by":"crossref","unstructured":"Marbouti F, Diefes-Dux HA, Strobel J (2015) Building course-specific regression-based models to identify at-risk students. In: ASEE Annual Conference and Exposition, pp 26\u2013304","DOI":"10.18260\/p.23643"},{"key":"2124_CR11","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.chb.2019.04.015","volume":"98","author":"X Xu","year":"2019","unstructured":"Xu X, Wang J, Peng H, Wu R (2019) Prediction of academic performance associated with internet usage behaviors using machine learning algorithms. Comput Hum Behav 98:166\u2013173","journal-title":"Comput Hum Behav"},{"key":"2124_CR12","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.chb.2017.01.047","volume":"73","author":"EB Costa","year":"2017","unstructured":"Costa EB, Fonseca B, Santana MA, Ara\u00fajo FF, Rego J (2017) Evaluating the effectiveness of educational data mining techniques for early prediction of students\u2019 academic failure in introductory programming courses. Comput Hum Behav 73:247\u2013256","journal-title":"Comput Hum Behav"},{"key":"2124_CR13","doi-asserted-by":"publisher","unstructured":"Sateesh\u00a0Nayani SRP, D, RL (2023) Combination of deep learning models for students\u2019 performance prediction with a development of entropy weighted rough set feature mining. Cybern Syst. https:\/\/doi.org\/10.1080\/01969722.2023.2166259","DOI":"10.1080\/01969722.2023.2166259"},{"key":"2124_CR14","doi-asserted-by":"crossref","unstructured":"Neha K, Kumar R, Jahangeer\u00a0Sidiq S, Zaman M (2023) Deep neural networks predicting student performance. In: Proceedings of the international conference on data science and applications, pp 71\u201379","DOI":"10.1007\/978-981-19-6634-7_6"},{"issue":"1","key":"2124_CR15","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans Neural Netw 20(1):61\u201380","journal-title":"IEEE Trans Neural Netw"},{"key":"2124_CR16","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.neunet.2020.06.006","volume":"129","author":"D Bacciu","year":"2020","unstructured":"Bacciu D, Errica F, Micheli A, Podda M (2020) A gentle introduction to deep learning for graphs. Neural Netw 129:203\u2013221","journal-title":"Neural Netw"},{"key":"2124_CR17","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: A review of methods and applications. AI Open 1:57\u201381","journal-title":"AI Open"},{"issue":"1","key":"2124_CR18","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2021","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2021) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4\u201324","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"2124_CR19","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1109\/TKDE.2020.2981333","volume":"34","author":"Z Zhang","year":"2022","unstructured":"Zhang Z, Cui P, Zhu W (2022) Deep learning on graphs: a survey. IEEE Trans Knowl Data Eng 34(1):249\u2013270","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2124_CR20","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s13042-023-01786-w","volume":"15","author":"Z Li","year":"2024","unstructured":"Li Z, Wang B, Huang J, Jin Y, Xu Z, Zhang J, Gao J (2024) A graph-powered large-scale fraud detection system. Int J Mach Learn Cybern 15:115\u2013128","journal-title":"Int J Mach Learn Cybern"},{"key":"2124_CR21","unstructured":"Yadati N, Nimishakavi M, Yadav P, Nitin V, Louis A, Talukdar P (2019) HyperGCN: A new method of training graph convolutional networks on hypergraphs. In: Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS), pp 1511\u20131522"},{"key":"2124_CR22","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: The 5th international conference on learning representations (ICLR)"},{"key":"2124_CR23","doi-asserted-by":"crossref","unstructured":"Feng Y, You H, Zhang Z, Ji R, Gao Y (2019) Hypergraph neural networks. In: Proceedings of the 33nd AAAI conference on artificial intelligence (AAAI), pp 3558\u20133565","DOI":"10.1609\/aaai.v33i01.33013558"},{"issue":"3","key":"2124_CR24","first-page":"3245","volume":"45","author":"H Wu","year":"2023","unstructured":"Wu H, Yan Y, Ng MK-P (2023) Hypergraph collaborative network on vertices and hyperedges. IEEE Trans Pattern Anal Mach Intell 45(3):3245\u20133258","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2124_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107637","volume":"110","author":"S Bai","year":"2021","unstructured":"Bai S, Zhang F, Torr PHS (2021) Hypergraph convolution and hypergraph attention. Pattern Recogn 110:107637","journal-title":"Pattern Recogn"},{"issue":"5","key":"2124_CR26","doi-asserted-by":"publisher","first-page":"1365","DOI":"10.1109\/TBDATA.2023.3278988","volume":"9","author":"Z Liu","year":"2023","unstructured":"Liu Z, Luo Y, Pu X, Min G, Luo C (2023) A multi-modal hypergraph neural network via parametric filtering and feature sampling. IEEE Trans Big Data 9(5):1365\u20131379","journal-title":"IEEE Trans Big Data"},{"key":"2124_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-68530-4","volume-title":"Framelets and wavelets","author":"B Han","year":"2017","unstructured":"Han B (2017) Framelets and wavelets. Analysis, and Applications, Applied and Numerical Harmonic Analysis. Birkh\u00e4user xxxiii Cham, Algorithms"},{"issue":"4","key":"2124_CR28","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1137\/19M1249734","volume":"12","author":"B Han","year":"2019","unstructured":"Han B, Mo Q, Zhao Z, Zhuang X (2019) Directional compactly supported tensor product complex tight framelets with applications to image denoising and inpainting. SIAM J Imag Sci 12(4):1739\u20131771","journal-title":"SIAM J Imag Sci"},{"key":"2124_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00041-021-09816-3","volume":"27","author":"Y Xiao","year":"2021","unstructured":"Xiao Y, Zhuang X (2021) Adaptive directional Haar tight framelets on bounded domains for digraph signal representations. J Fourier Anal Appl 27:1\u201326","journal-title":"J Fourier Anal Appl"},{"key":"2124_CR30","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160169","author":"J Li","year":"2022","unstructured":"Li J, Feng H, Zhuang X (2022) Convolutional neural networks for spherical signal processing via area-regular spherical Haar tight framelets. IEEE Transactions on Neural Networks and Learning Systems. https:\/\/doi.org\/10.1109\/TNNLS.2022.3160169","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"2124_CR31","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1016\/j.acha.2022.04.003","volume":"60","author":"Y-R Li","year":"2022","unstructured":"Li Y-R, Shen L, Zhuang X (2022) A tailor-made 3-dimensional directional Haar semi-tight framelet for pMRI reconstruction. Appl Comput Harmon Anal 60:446\u2013470","journal-title":"Appl Comput Harmon Anal"},{"issue":"2","key":"2124_CR32","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/j.acha.2015.07.003","volume":"41","author":"B Han","year":"2016","unstructured":"Han B, Zhao Z, Zhuang X (2016) Directional tensor product complex tight framelets with low redundancy. Appl Comput Harmon Anal 41(2):603\u2013637","journal-title":"Appl Comput Harmon Anal"},{"issue":"3","key":"2124_CR33","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1137\/15M1048318","volume":"9","author":"X Zhuang","year":"2016","unstructured":"Zhuang X (2016) Digital affine shear transforms: fast realization and applications in image\/video processing. SIAM J Imag Sci 9(3):1437\u20131466","journal-title":"SIAM J Imag Sci"},{"key":"2124_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-0185-2","volume-title":"Hypergraph Computation","author":"Q Dai","year":"2023","unstructured":"Dai Q, Gao Y (2023) Hypergraph Computation. Springer, Singapore"},{"key":"2124_CR35","unstructured":"Yadati N, Nimishakavi M, Yadav P, Nitin V, Louis A, Talukdar P HyperGCN: A new method for training graph convolutional networks on hypergraphs. In: Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS), pp. 1511\u20131522"},{"key":"2124_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107637","volume":"110","author":"S Bai","year":"2021","unstructured":"Bai S, Zhang F, Torr PH (2021) Hypergraph convolution and hypergraph attention. Pattern Recogn 110:107637","journal-title":"Pattern Recogn"},{"key":"2124_CR37","doi-asserted-by":"crossref","unstructured":"Huang J, Yang J (2021) UniGNN: a unified framework for graph and hypergraph neural networks. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), pp. 2563\u20132569","DOI":"10.24963\/ijcai.2021\/353"},{"key":"2124_CR38","unstructured":"Dong Y, Sawin W, Bengio Y (2020) HNHN: Hypergraph networks with hyperedge neurons. In: Graph Representation Learning and Beyond Workshop at ICML 2020"},{"key":"2124_CR39","unstructured":"Prokopchik K, Benson AR, Tudisco F (2022) Nonlinear feature diffusion on hypergraphs. In: Proceedings of the 39th International Conference on Machine Learning (ICML), pp. 17945\u201317958"},{"key":"2124_CR40","unstructured":"Wang P, Yang, S, Liu Y, Wang Z, Li P (2022) Equivariant hypergraph diffusion neural operators. arXiv preprint arXiv:2207.06680"},{"key":"2124_CR41","unstructured":"Chien E, Pan C, Peng J, Milenkovic O (2022) You are allset: A multiset function framework for hypergraph neural networks. In: The 10th International Conference on Learning Representations (ICLR)"},{"issue":"3","key":"2124_CR42","first-page":"3245","volume":"45","author":"H Wu","year":"2023","unstructured":"Wu H, Yan Y, Ng MK-P (2023) Hypergraph collaborative network on vertices and hyperedges. IEEE Trans Pattern Anal Mach Intell 45(3):3245\u20133258","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2124_CR43","doi-asserted-by":"crossref","unstructured":"Tang B, Chen S, Dong X (2024) Hypergraph-MLP: Learning on hypergraphs without message passing. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","DOI":"10.1109\/ICASSP48485.2024.10446277"},{"key":"2124_CR44","doi-asserted-by":"crossref","unstructured":"Liu Z, Tang B, Ye Z, Dong X, Chen, S, Wang Y (2024) Hypergraph transformer for semi-supervised classification. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","DOI":"10.1109\/ICASSP48485.2024.10446248"},{"key":"2124_CR45","doi-asserted-by":"crossref","unstructured":"Zhang D, Chen F, Chen X (2023) DualGATs: Dual graph attention networks for emotion recognition in conversations. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), pp. 7395\u20137408","DOI":"10.18653\/v1\/2023.acl-long.408"},{"key":"2124_CR46","unstructured":"Cortez P, Silva AMG (2008) Using data mining to predict secondary school student performance. In: Proceedings of the 5th Future Business Technology Conference, pp. 5\u201312"},{"issue":"1","key":"2124_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.171","volume":"4","author":"J Kuzilek","year":"2017","unstructured":"Kuzilek J, Hlosta M, Zdrahal Z (2017) Open university learning analytics dataset. Scientific Data 4(1):1\u20138","journal-title":"Scientific Data"},{"key":"2124_CR48","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.neunet.2021.01.011","volume":"137","author":"C Huang","year":"2021","unstructured":"Huang C, Li M, Wang D (2021) Stochastic configuration network ensembles with selective base models. Neural Netw 137:106\u2013118","journal-title":"Neural Netw"},{"issue":"13","key":"2124_CR49","doi-asserted-by":"publisher","first-page":"7965","DOI":"10.3390\/su14137965","volume":"14","author":"M Li","year":"2022","unstructured":"Li M, Wang X, Wang Y, Chen Y, Chen Y (2022) Study-GNN: a novel pipeline for student performance prediction based on multi-topology graph neural networks. Sustainability 14(13):7965","journal-title":"Sustainability"},{"key":"2124_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102224","volume":"105","author":"M Li","year":"2024","unstructured":"Li M, Zhuang X, Bai L, Ding W (2024) Multimodal graph learning based on 3D haar semi-tight framelet for student engagement prediction. Information Fusion 105:102224","journal-title":"Information Fusion"},{"issue":"12","key":"2124_CR51","doi-asserted-by":"publisher","first-page":"5659","DOI":"10.1109\/TIP.2015.2487860","volume":"24","author":"C Hong","year":"2015","unstructured":"Hong C, Yu J, Wan J, Tao D, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659\u20135670","journal-title":"IEEE Trans Image Process"},{"issue":"7","key":"2124_CR52","doi-asserted-by":"publisher","first-page":"3952","DOI":"10.1109\/TII.2018.2884211","volume":"15","author":"C Hong","year":"2019","unstructured":"Hong C, Yu J, Zhang J, Jin X, Lee K-H (2019) Multimodal face-pose estimation with multitask manifold deep learning. IEEE Trans Industr Inf 15(7):3952\u20133961","journal-title":"IEEE Trans Industr Inf"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02124-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02124-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02124-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T08:29:05Z","timestamp":1723883345000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02124-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,13]]},"references-count":52,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["2124"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02124-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,13]]},"assertion":[{"value":"5 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors affirm that there are no known financial conflicts of interest or personal connections that might be perceived as influencing the findings presented in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}