{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T16:16:50Z","timestamp":1771949810299,"version":"3.50.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T00:00:00Z","timestamp":1701820800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T00:00:00Z","timestamp":1701820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106070"],"award-info":[{"award-number":["62106070"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00521-023-09268-4","type":"journal-article","created":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T18:03:15Z","timestamp":1701885795000},"page":"3681-3698","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi-level contrastive graph learning for academic abnormality prediction"],"prefix":"10.1007","volume":"36","author":[{"given":"Yong","family":"Ouyang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanlin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7935-7173","authenticated-orcid":false,"given":"Rong","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yawen","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinhang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwei","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,6]]},"reference":[{"key":"9268_CR1","unstructured":"Wang S (2014) Analysis of educational data mining to promote personalized learning pathways for college students. Exam Weekly, pp 176"},{"issue":"6","key":"9268_CR2","doi-asserted-by":"publisher","first-page":"1683","DOI":"10.1007\/s00521-018-3756-y","volume":"31","author":"IE Livieris","year":"2019","unstructured":"Livieris IE, Kotsilieris T, Tampakas V et al (2019) Improving the evaluation process of students\u2019 performance utilizing a decision support software. Neural Comput Appl 31(6):1683\u20131694","journal-title":"Neural Comput Appl"},{"key":"9268_CR3","doi-asserted-by":"publisher","first-page":"140731","DOI":"10.1109\/ACCESS.2021.3119596","volume":"9","author":"A Nabil","year":"2021","unstructured":"Nabil A, Seyam M, Abou-Elfetouh Ahmed (2021) Prediction of students\u2019 academic performance based on courses grades using deep neural networks. IEEE Access 9:140731\u2013140746","journal-title":"IEEE Access"},{"key":"9268_CR4","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.chb.2014.09.034","volume":"47","author":"W Xing","year":"2015","unstructured":"Xing W, Guo R, Petakovic E et al (2015) Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory. Comput Human Behav 47:168\u2013181","journal-title":"Comput Human Behav"},{"key":"9268_CR5","doi-asserted-by":"publisher","first-page":"19558","DOI":"10.1109\/ACCESS.2022.3151652","volume":"10","author":"G Feng","year":"2022","unstructured":"Feng G, Fan M, Chen Y (2022) Analysis and prediction of students\u2019 academic performance based on educational data mining. IEEE Access 10:19558\u201319571","journal-title":"IEEE Access"},{"key":"9268_CR6","unstructured":"Tao B, Liu K, Miao F et al (2019) Design of Early Warning System for Student\u2019s Poor Academic Performance Based on SVM Improved by KFCM. Research And Exploration In Laboratory 38(5)"},{"issue":"2","key":"9268_CR7","first-page":"1","volume":"43","author":"Z Sun","year":"2016","unstructured":"Sun Z, Lu C, Shi Z et al (2016) Research and advances on deep learning. Comput Sci 43(2):1\u20138","journal-title":"Comput Sci"},{"issue":"4","key":"9268_CR8","first-page":"199","volume":"30","author":"Jiang Shaoping","year":"2021","unstructured":"Shaoping Jiang (2021) Correlation analysis of student behavior and improvement of GA-BP academic early warning algorithm. Comput Syst Appl 30(4):199\u2013203","journal-title":"Comput Syst Appl"},{"key":"9268_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107271","volume":"93","author":"A Mubarak","year":"2021","unstructured":"Mubarak A, Cao H, Hezam I (2021) Deep analytic model for student dropout prediction in massive open online courses. Comput Electr Eng 93:107271","journal-title":"Comput Electr Eng"},{"issue":"10","key":"9268_CR10","doi-asserted-by":"publisher","first-page":"3023","DOI":"10.1007\/s10489-020-01692-6","volume":"50","author":"Y Zeng","year":"2020","unstructured":"Zeng Y, Ouyang Y, Gao R et al (2020) Elective future: the influence factor mining of students\u2019 graduation development based on hierarchical attention neural network model with graph. Appl Intell 50(10):3023\u20133039","journal-title":"Appl Intell"},{"issue":"2","key":"9268_CR11","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1007\/s00521-020-05045-9","volume":"33","author":"P Hai-tao","year":"2021","unstructured":"Hai-tao P, Ming-qu F, Hong-bin Z et al (2021) Predicting academic performance of students in Chinese-foreign cooperation in running schools with graph convolutional network. Neural Comput Appl 33(2):637\u2013645","journal-title":"Neural Comput Appl"},{"key":"9268_CR12","doi-asserted-by":"crossref","unstructured":"Liu M, Shao P, Zhang K (2021) Graph-Based Exercise-and Knowledge-Aware Learning Network for Student Performance Prediction. CAAI International Conference on Artificial Intelligence, pp 27-38","DOI":"10.1007\/978-3-030-93046-2_3"},{"key":"9268_CR13","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.neucom.2020.12.023","volume":"431","author":"X Lu","year":"2021","unstructured":"Lu X, Zhu Y, Xu Y et al (2021) Learning from multiple dynamic graphs of student and course interactions for student grade predictions. Neurocomputing 431:23\u201333","journal-title":"Neurocomputing"},{"key":"9268_CR14","unstructured":"Tan G, Xu F, Qu W (2016) Factors and models influencing students\u2019 behavioral intention to teach online in higher education. e-Education Research, pp 47\u201353"},{"issue":"2","key":"9268_CR15","first-page":"64","volume":"22","author":"LP Chi","year":"2006","unstructured":"Chi LP, Xin ZQ (2006) Measurement of college students\u2019 learning motivation and its relationship with self-efficacy. Psychol Dev Edu 22(2):64\u201370","journal-title":"Psychol Dev Edu"},{"key":"9268_CR16","first-page":"46","volume":"3","author":"XM He","year":"2008","unstructured":"He XM, Chen XM (2008) A study on the influence of students\u2019 learning engagement on learning interest. Global Edu Outlook 3:46\u201351","journal-title":"Global Edu Outlook"},{"key":"9268_CR17","first-page":"62","volume":"24","author":"J Zhang","year":"2019","unstructured":"Zhang J, Chen L (2019) Clustering-based undersampling with random over sampling examples and support vector machine for imbalanced classification of breast cancer diagnosis. Comput Assist Surgery 24:62\u201372","journal-title":"Comput Assist Surgery"},{"key":"9268_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106087","volume":"203","author":"J Zhao","year":"2020","unstructured":"Zhao J, Jin J, Chen S et al (2020) A weighted hybrid ensemble method for classifying imbalanced data. Knowl Based Syst 203:106087","journal-title":"Knowl Based Syst"},{"key":"9268_CR19","doi-asserted-by":"crossref","unstructured":"Chen J, Shen Y, Ali R (2018) Credit card fraud detection using sparse autoencoder and generative adversarial network. Electronics and Mobile Communication Conference, pp. 1054\u20131059","DOI":"10.1109\/IEMCON.2018.8614815"},{"key":"9268_CR20","doi-asserted-by":"crossref","unstructured":"Zaccagnino R, Capo C, Guarino A, et al (2021) Credit card fraud detection using sparse autoencoder and generative adversarial network. Multimedia Tools and Applications, pp. 15803\u201315824","DOI":"10.1007\/s11042-020-10446-y"},{"key":"9268_CR21","doi-asserted-by":"crossref","unstructured":"Guarino A, Lettieri N, Malandrino D, et al (2022) Adam or eve? Automatic users\u2019 gender classification via gestures analysis on touch devices. Neural Computing and Applications, pp. 18473\u201318495","DOI":"10.1007\/s00521-022-07454-4"},{"issue":"4","key":"9268_CR22","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1137\/070710111","volume":"51","author":"A Clauset","year":"2009","unstructured":"Clauset A, Shalizi CR, Newman MEJ (2009) Power-law distributions in empirical data. SIAM Rev 51(4):661\u2013703","journal-title":"SIAM Rev"},{"issue":"12","key":"9268_CR23","doi-asserted-by":"publisher","first-page":"2417","DOI":"10.1002\/asi.21426","volume":"61","author":"S Milojevi\u0107","year":"2010","unstructured":"Milojevi\u0107 S (2010) Power law distributions in information science: making the case for logarithmic binning. J Am Soc Information Sci Technol 61(12):2417\u20132425","journal-title":"J Am Soc Information Sci Technol"},{"key":"9268_CR24","doi-asserted-by":"crossref","unstructured":"Yang G, Ouyang Y, Ye Z, et al (2022) Social-path embedding-based transformer for graduation development prediction. Applied Intelligence, pp 1-18","DOI":"10.1007\/s10489-022-03268-y"},{"key":"9268_CR25","doi-asserted-by":"crossref","unstructured":"Zhao, T, Xiang Z, Wang S (2021) Graphsmote: Imbalanced node classification on graphs with graph neural networks. Proceedings of the 14th ACM international conference on web search and data mining, pp 833\u2013841","DOI":"10.1145\/3437963.3441720"},{"key":"9268_CR26","doi-asserted-by":"crossref","unstructured":"Jin M, Zheng Y, Li Y F, et al (2021) Multi-scale contrastive siamese networks for self-supervised graph representation learning. In: proceedings of the thirtieth international joint conference on artificial intelligence, IJCAI-21, pp 1477\u20131483","DOI":"10.24963\/ijcai.2021\/204"},{"key":"9268_CR27","doi-asserted-by":"crossref","unstructured":"Chen X, He K (2021) Exploring simple siamese representation learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 15750\u201315758","DOI":"10.1109\/CVPR46437.2021.01549"},{"issue":"3","key":"9268_CR28","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1109\/TLT.2020.2988253","volume":"13","author":"Z Yang","year":"2020","unstructured":"Yang Z, Yang J, Rice K et al (2020) Using convolutional neural network to recognize learning images for early warning of at-risk students. IEEE Trans Learn Technol 13(3):617\u2013630","journal-title":"IEEE Trans Learn Technol"},{"issue":"1","key":"9268_CR29","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s00521-018-3802-9","volume":"32","author":"J Kong","year":"2020","unstructured":"Kong J, Han J, Ding J et al (2020) Analysis of students\u2019 learning and psychological features by contrast frequent patterns mining on academic performance. Neural Comput Appl 32(1):205\u2013211","journal-title":"Neural Comput Appl"},{"issue":"11","key":"9268_CR30","first-page":"8","volume":"36","author":"RR Kabra","year":"2011","unstructured":"Kabra RR, Bichkar RS (2011) Performance prediction of engineering students using decision trees. Int J Comput Appl 36(11):8\u201312","journal-title":"Int J Comput Appl"},{"key":"9268_CR31","first-page":"104","volume":"164","author":"H Chang","year":"2021","unstructured":"Chang H, Kim H (2021) Predicting the pass probability of secondary school students taking online classes. Comput Edu 164:104\u2013110","journal-title":"Comput Edu"},{"issue":"4","key":"9268_CR32","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1109\/TE.2020.2984900","volume":"63","author":"M Tadayon","year":"2020","unstructured":"Tadayon M, Pottie G (2020) Predicting student performance in an educational game using a hidden markov model. IEEE Trans Edu 63(4):299\u2013304","journal-title":"IEEE Trans Edu"},{"key":"9268_CR33","doi-asserted-by":"publisher","first-page":"87370","DOI":"10.1109\/ACCESS.2021.3088152","volume":"9","author":"H Prabowo","year":"2021","unstructured":"Prabowo H, Hidayat A, Cenggoro T et al (2021) Aggregating time series and tabular data in deep learning model for university students\u2019 gpa prediction. IEEE Access 9:87370\u201387377","journal-title":"IEEE Access"},{"issue":"1","key":"9268_CR34","first-page":"62","volume":"38","author":"Q Jiang","year":"2017","unstructured":"Jiang Q, Zhao W, Zhao W et al (2017) Empirical research of predictive factors and intervention countermeasures of online learning performance on big data-based learning analytics. e-Edu Res 38(1):62\u201369","journal-title":"e-Edu Res"},{"key":"9268_CR35","doi-asserted-by":"crossref","unstructured":"Wu K, Edwards A, Fan W, et al (2014) Classifying imbalanced data streams via dynamic feature group weighting with importance sampling. Proceedings of the 2014 SIAM international conference on data mining, pp 722\u2013730","DOI":"10.1137\/1.9781611973440.83"},{"key":"9268_CR36","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO et al (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"9268_CR37","first-page":"231","volume":"2011","author":"CX Ling","year":"2008","unstructured":"Ling CX, Sheng VS (2008) Cost-sensitive learning and the class imbalance problem. Encyclop Mach Learn 2011:231\u2013235","journal-title":"Encyclop Mach Learn"},{"issue":"1","key":"9268_CR38","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1109\/TKDE.2006.17","volume":"18","author":"ZH Zhou","year":"2005","unstructured":"Zhou ZH, Liu XY (2005) Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans Knowl Data Eng 18(1):63\u201377","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"9","key":"9268_CR39","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263\u20131284","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"11","key":"9268_CR40","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y et al (1998) Gradient-based learning applied to document recognition. Proceed IEEE 86(11):2278\u20132324","journal-title":"Proceed IEEE"},{"issue":"6","key":"9268_CR41","first-page":"1229","volume":"40","author":"F Zhou","year":"2017","unstructured":"Zhou F, Jin L, Dong J (2017) Review of convolutional neural network. Chin J Comput 40(6):1229\u20131251","journal-title":"Chin J Comput"},{"issue":"3","key":"9268_CR42","first-page":"453","volume":"42","author":"S Zhang","year":"2019","unstructured":"Zhang S, Gong Y, Wang J (2019) The development of deep convolution neural network and its applications on computer vision. Chin J Comput 42(3):453\u2013482","journal-title":"Chin J Comput"},{"issue":"1","key":"9268_CR43","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/72.554195","volume":"8","author":"S Lawrence","year":"1997","unstructured":"Lawrence S, Giles CL, Tsoi AC et al (1997) Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw 8(1):98\u2013113","journal-title":"IEEE Trans Neural Netw"},{"key":"9268_CR44","unstructured":"Ciresan D C, Meier U, Masci J, et al (2011) Flexible, high performance convolutional neural networks for image classification. Twenty-second international joint conference on artificial intelligence"},{"issue":"5","key":"9268_CR45","first-page":"755","volume":"43","author":"BB Xu","year":"2020","unstructured":"Xu BB, Sham CT, Huang JJ, Shen HW, Cheng XQ (2020) A survey on graph convolutional neural network. Chin J Comput 43(5):755\u2013780","journal-title":"Chin J Comput"},{"key":"9268_CR46","doi-asserted-by":"publisher","first-page":"87857","DOI":"10.1109\/ACCESS.2021.3063475","volume":"8","author":"M Lu","year":"2021","unstructured":"Lu M, Wang Y, Tan D et al (2021) Student program classification using gated graph attention neural network. IEEE Access 8:87857\u201387868","journal-title":"IEEE Access"},{"issue":"13","key":"9268_CR47","first-page":"251","volume":"57","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Lu M, Zheng Y, Li HF (2021) Student grade prediction based on graph auto-encoder model. Comput Eng Appl 57(13):251\u2013257","journal-title":"Comput Eng Appl"},{"key":"9268_CR48","first-page":"5812","volume":"33","author":"Y You","year":"2020","unstructured":"You Y, Chen T, Sui Y et al (2020) Graph contrastive learning with augmentations. Adv Neural Information Process Syst 33:5812\u20135823","journal-title":"Adv Neural Information Process Syst"},{"key":"9268_CR49","unstructured":"Chen T, Kornblith S, Norouzi M, et al (2020) A simple framework for contrastive learning of visual representations. International conference on machine learning, pp 1597\u20131607"},{"key":"9268_CR50","doi-asserted-by":"crossref","unstructured":"More A S, Rana D P (2017) Review of random forest classification techniques to resolve data imbalance. 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM), pp 72\u201378","DOI":"10.1109\/ICISIM.2017.8122151"},{"key":"9268_CR51","doi-asserted-by":"crossref","unstructured":"Da C, Xu S, Ding K, et al (2017) AMVH: Asymmetric multi-valued hashing. Proceedings of the IEEE conference on computer vision and pattern recognition, pp 736\u2013744","DOI":"10.1109\/CVPR.2017.102"},{"issue":"6","key":"9268_CR52","doi-asserted-by":"publisher","first-page":"745","DOI":"10.3724\/SP.J.1004.2013.00745","volume":"39","author":"Y Cao","year":"2013","unstructured":"Cao Y, Miao QG, Liu JC et al (2013) Advance and prospects of adaboost algorithm. Acta Autom Sin 39(6):745\u2013758","journal-title":"Acta Autom Sin"},{"issue":"5","key":"9268_CR53","doi-asserted-by":"publisher","first-page":"6055","DOI":"10.3233\/JIFS-179188","volume":"37","author":"N Yang","year":"2019","unstructured":"Yang N (2019) Construction of artificial translation grading model based on BP neural network in college students\u2019 translation grading system. J Intell Fuzzy Syst 37(5):6055\u20136062","journal-title":"J Intell Fuzzy Syst"},{"key":"9268_CR54","first-page":"20","volume":"1050","author":"P Velickovic","year":"2017","unstructured":"Velickovic P, Cucurull G, Casanova A et al (2017) Graph attention networks. Stat 1050:20","journal-title":"Stat"},{"issue":"8","key":"9268_CR55","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Computat 9(8):1735\u20131780","journal-title":"Neural Computat"},{"key":"9268_CR56","doi-asserted-by":"crossref","unstructured":"Wu J, Wang X, Feng F, et al (2021) Self-supervised graph learning for recommendation. Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp 726-735","DOI":"10.1145\/3404835.3462862"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09268-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09268-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09268-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T12:11:08Z","timestamp":1707480668000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09268-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,6]]},"references-count":56,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["9268"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09268-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,6]]},"assertion":[{"value":"22 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}