{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:34:48Z","timestamp":1778693688744,"version":"3.51.4"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"30","license":[{"start":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T00:00:00Z","timestamp":1691452800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T00:00:00Z","timestamp":1691452800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Beijing Educational Science Planning Project under grant","award":["CHCA2020102"],"award-info":[{"award-number":["CHCA2020102"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00521-023-08894-2","type":"journal-article","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T09:02:13Z","timestamp":1691485333000},"page":"22341-22357","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["CNN autoencoders and LSTM-based reduced order model for student dropout prediction"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1004-3613","authenticated-orcid":false,"given":"Ke","family":"Niu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqiang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueping","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingni","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,8]]},"reference":[{"key":"8894_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107271","volume":"93","author":"AA Mubarak","year":"2021","unstructured":"Mubarak AA, Cao H, Hezam IM (2021) Deep analytic model for student dropout prediction in massive open online courses. Comput Electr Eng 93:107271. https:\/\/doi.org\/10.1016\/j.compeleceng.2021.107271","journal-title":"Comput Electr Eng"},{"key":"8894_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107315","volume":"94","author":"Q Fu","year":"2021","unstructured":"Fu Q, Gao Z, Zhou J et al (2021) CLSA: a novel deep learning model for MOOC dropout prediction. Comput Electr Eng 94:107315. https:\/\/doi.org\/10.1016\/j.compeleceng.2021.107315","journal-title":"Comput Electr Eng"},{"key":"8894_CR3","doi-asserted-by":"crossref","unstructured":"Sinha T, Jermann P, Li N, et\u00a0al. (2014) Your click decides your fate: Inferring information processing and attrition behavior from mooc video clickstream interactions. arXiv:1407.7131","DOI":"10.3115\/v1\/W14-4102"},{"issue":"01","key":"8894_CR4","first-page":"517","volume":"33","author":"W Feng","year":"2019","unstructured":"Feng W, Tang J, Liu TX (2019) Understanding dropouts in MOOCs. Proc AAAI Conf Artif Intell 33(01):517\u2013524.","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"8894_CR5","unstructured":"Qi Y, Wu Q, Wang H, et al. (2018) Bandit learning with implicit feedback. Adv Neural Inf Process Syst 31."},{"key":"8894_CR6","doi-asserted-by":"publisher","DOI":"10.1108\/IJWIS-11-2018-0080","author":"Y Mourdi","year":"2019","unstructured":"Mourdi Y, Sadgal M, El Kabtane H et al (2019) A machine learning-based methodology to predict learners\u2019 dropout, success or failure in MOOCs. Int J Web Inf Syst. https:\/\/doi.org\/10.1108\/IJWIS-11-2018-0080","journal-title":"Int J Web Inf Syst"},{"key":"8894_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2021.104189","volume":"167","author":"J Zhang","year":"2021","unstructured":"Zhang J, Gao M, Zhang J (2021) The learning behaviours of dropouts in MOOCs: a collective attention network perspective. Comput Edu 167:104189. https:\/\/doi.org\/10.1016\/j.compedu.2021.104189","journal-title":"Comput Edu"},{"key":"8894_CR8","doi-asserted-by":"publisher","first-page":"103728.1","DOI":"10.1016\/j.compedu.2019.103728","volume":"145","author":"PM Moreno-Marcos","year":"2020","unstructured":"Moreno-Marcos PM, Munoz-Merino PJ, Maldonado-Mahauad J et al (2020) Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs. Comput Edu 145:103728.1-103728.15. https:\/\/doi.org\/10.1016\/j.compedu.2019.103728","journal-title":"Comput Edu"},{"issue":"1","key":"8894_CR9","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/ACCESS.2020.3045157","volume":"5","author":"JL Hung","year":"2015","unstructured":"Hung JL, Wang MC, Wang S et al (2015) Identifying at-risk students for early interventions-A time-series clustering approach. IEEE Trans Emerg Topics Comput 5(1):45\u201355. https:\/\/doi.org\/10.1109\/ACCESS.2020.3045157","journal-title":"IEEE Trans Emerg Topics Comput"},{"issue":"1","key":"8894_CR10","doi-asserted-by":"publisher","first-page":"8971","DOI":"10.1007\/s00500-021-05795-1","volume":"25","author":"C Jin","year":"2021","unstructured":"Jin C (2021) Dropout prediction model in MOOC based on clickstream data and student sample weight. Soft Computing 25(1):8971\u20138988. https:\/\/doi.org\/10.1007\/s00500-021-05795-1","journal-title":"Soft Computing"},{"key":"8894_CR11","doi-asserted-by":"publisher","first-page":"225324","DOI":"10.1109\/ACCESS.2020.3045157","volume":"8","author":"Y Zheng","year":"2020","unstructured":"Zheng Y, Gao Z, Wang Y et al (2020) MOOC dropout prediction using FWTS-CNN model based on fused feature weighting and time series. IEEE Access 8:225324\u2013225335. https:\/\/doi.org\/10.1109\/ACCESS.2020.3045157","journal-title":"IEEE Access"},{"key":"8894_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93846-2_70","author":"G Wang","year":"2018","unstructured":"Wang G, Tang Y, Li J et al (2018) Modeling student learning Behaviors in ALEKS: A two-layer hidden Markov modeling approach. Int Conf Artif Intell Edu. https:\/\/doi.org\/10.1007\/978-3-319-93846-2_70","journal-title":"Int Conf Artif Intell Edu."},{"key":"8894_CR13","doi-asserted-by":"publisher","unstructured":"Shaleena KP, Paul S (2015) Data mining techniques for predicting student performance. 2015 IEEE international conference on engineering and technology (ICETECH). IEEE:1-3. https:\/\/doi.org\/10.1109\/ICETECH.2015.7275025","DOI":"10.1109\/ICETECH.2015.7275025"},{"key":"8894_CR14","doi-asserted-by":"crossref","unstructured":"Sinha T, Jermann P, Li N, et\u00a0al. (2014) Your click decides your fate: Inferring information processing and attrition behavior from mooc video clickstream interactions. arXiv:1407.7131","DOI":"10.3115\/v1\/W14-4102"},{"key":"8894_CR15","unstructured":"Taylor C, Veeramachaneni K, O\u2019Reilly UM. Likely to stop? predicting stopout in massive open online courses. arXiv:1408.3382"},{"issue":"20","key":"8894_CR16","doi-asserted-by":"publisher","first-page":"10287","DOI":"10.1007\/s00500-018-3581-3","volume":"23","author":"L Qiu","year":"2019","unstructured":"Qiu L, Liu Y, Hu Q et al (2019) Student dropout prediction in massive open online courses by convolutional neural networks. Soft Computing 23(20):10287\u201310301. https:\/\/doi.org\/10.1007\/s00500-018-3581-3","journal-title":"Soft Computing"},{"key":"8894_CR17","doi-asserted-by":"publisher","unstructured":"Sun D, Mao Y, Du J, et al (2019) Deep learning for dropout prediction in MOOCs. Eighth International Conference on Educational Innovation through Technology (EITT). IEEE:87-90. https:\/\/doi.org\/10.1109\/EITT.2019.00025","DOI":"10.1109\/EITT.2019.00025"},{"key":"8894_CR18","doi-asserted-by":"publisher","unstructured":"Fei M, Yeung DY (2015) Temporal models for predicting student dropout in massive open online courses. 2015 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE:256-263. https:\/\/doi.org\/10.1109\/ICDMW.2015.174","DOI":"10.1109\/ICDMW.2015.174"},{"issue":"1","key":"8894_CR19","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1774\/1\/012065","volume":"1774","author":"M Chen","year":"2021","unstructured":"Chen M, Wu L (2021) A dropout prediction method based on time series model in MOOCs. J Phys Conf Series. 1774(1):012065. https:\/\/doi.org\/10.1088\/1742-6596\/1774\/1\/012065","journal-title":"J Phys Conf Series."},{"issue":"3","key":"8894_CR20","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1177\/0735633118757015","volume":"57","author":"W Xing","year":"2019","unstructured":"Xing W, Du D (2019) Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention. J Edu Comput Res. 57(3):547\u2013570. https:\/\/doi.org\/10.1177\/0735633118757015","journal-title":"J Edu Comput Res."},{"key":"8894_CR21","doi-asserted-by":"publisher","first-page":"2885","DOI":"10.1007\/s11063-022-10745-5","volume":"54","author":"C Xu","year":"2022","unstructured":"Xu C, Zhu G, Ye J et al (2022) Educational data mining: dropout prediction in XuetangX MOOCs. Neural Processing Lett 54:2885\u20132900. https:\/\/doi.org\/10.1007\/s11063-022-10745-5","journal-title":"Neural Processing Lett"},{"issue":"4","key":"8894_CR22","doi-asserted-by":"publisher","first-page":"2683","DOI":"10.1002\/int.22414","volume":"37","author":"C Blundo","year":"2022","unstructured":"Blundo C, Fenza G, Fuccio G et al (2022) A time-driven FCA-based approach for identifying students\u2019 dropout in MOOCs. Int J Intell Syst 37(4):2683\u20132705. https:\/\/doi.org\/10.1002\/int.22414","journal-title":"Int J Intell Syst"},{"key":"8894_CR23","doi-asserted-by":"publisher","unstructured":"Sharkey M, Sanders R (2014) A process for predicting MOOC attrition. Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs 50\u201354. https:\/\/doi.org\/10.3115\/v1\/W14-4109","DOI":"10.3115\/v1\/W14-4109"},{"key":"8894_CR24","doi-asserted-by":"publisher","unstructured":"Baranyi M, Nagy M, Molontay R (2020) Interpretable deep learning for university dropout prediction. Proceedings of the 21st annual conference on information technology education. pp 13\u201319. https:\/\/doi.org\/10.1145\/3368308.3415382","DOI":"10.1145\/3368308.3415382"},{"issue":"1","key":"8894_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3458023","volume":"16","author":"H Liu","year":"2021","unstructured":"Liu H, Zhu Y, Zang T et al (2021) Jointly modeling heterogeneous student behaviors and interactions among multiple prediction tasks. ACM Trans Knowl Discov Data (TKDD) 16(1):1\u201324. https:\/\/doi.org\/10.1145\/3458023","journal-title":"ACM Trans Knowl Discov Data (TKDD)"},{"key":"8894_CR26","doi-asserted-by":"publisher","unstructured":"Wen Y, Tian Y, Wen B et al. (2020) Consideration of the local correlation of learning behaviors to predict dropouts from MOOCs. inTsinghua Sci Technol 25(3), 336\u2013347. https:\/\/doi.org\/10.26599\/TST.2019.9010013","DOI":"10.26599\/TST.2019.9010013"},{"issue":"8","key":"8894_CR27","doi-asserted-by":"publisher","first-page":"61","DOI":"10.4236\/jcc.2021.98005","volume":"9","author":"Y Xie","year":"2021","unstructured":"Xie Y (2021) Student performance prediction via attention-based multi-layer long-short term memory. J Comput Commun 9(8):61\u201379.","journal-title":"J Comput Commun"},{"key":"8894_CR28","first-page":"1793","volume":"14","author":"K Niu","year":"2022","unstructured":"Niu K, Jia B, Zhou Y et al (2022) A hybrid model for predicting academic performance of engineering undergraduates. Int J Model Simul Sci Comput 14:1793\u20139623.","journal-title":"Int J Model Simul Sci Comput"},{"key":"8894_CR29","doi-asserted-by":"publisher","unstructured":"Kloft M, Stiehler F, Zheng Z, et al. (2014) Predicting MOOC dropout over weeks using machine learning methods. Proceedings of the EMNLP 2014 workshop on analysis of large scale social interaction in MOOCs 60-65. https:\/\/doi.org\/10.3115\/v1\/W14-4111","DOI":"10.3115\/v1\/W14-4111"},{"key":"8894_CR30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9471","author":"J He","year":"2015","unstructured":"He J, Bailey J, Rubinstein B et al (2015) Identifying at-risk students in massive open online courses. Proc AAAI Conf Artif Intell. https:\/\/doi.org\/10.1609\/aaai.v29i1.9471","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"8894_CR31","doi-asserted-by":"publisher","unstructured":"Liu T, Li X (2017) Finding out reasons for low completion in MOOC environment: an explicable approach using hybrid data mining methods. 2017 International Conference on Modern Education and Information Technology (MEIT 2017) 376-384. https:\/\/doi.org\/10.12783\/dtssehs\/meit2017\/12893","DOI":"10.12783\/dtssehs\/meit2017\/12893"},{"issue":"20","key":"8894_CR32","doi-asserted-by":"publisher","first-page":"10287","DOI":"10.1007\/s00500-018-3581-3","volume":"23","author":"L Qiu","year":"2019","unstructured":"Qiu L, Liu Y, Hu Q et al (2019) Student dropout prediction in massive open online courses by convolutional neural networks. Soft Computing 23(20):10287\u201310301. https:\/\/doi.org\/10.1007\/s00500-018-3581-3","journal-title":"Soft Computing"},{"key":"8894_CR33","doi-asserted-by":"publisher","unstructured":"Salekshahrezaee LJ, Khoshgoftaar T(2021) Feature Extraction for Class Imbalance Using a Convolutional Autoencoder and Data Sampling. 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) pp. 217-223. https:\/\/doi.org\/10.1109\/ICTAI52525.2021.00037","DOI":"10.1109\/ICTAI52525.2021.00037"},{"key":"8894_CR34","doi-asserted-by":"publisher","first-page":"15343","DOI":"10.1038\/s41598-021-93543-8","volume":"11","author":"F Khozeimeh","year":"2021","unstructured":"Khozeimeh F, Sharifrazi D, Izadi NH et al (2021) Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Sci Rep. 11:15343. https:\/\/doi.org\/10.1038\/s41598-021-93543-8","journal-title":"Sci Rep."},{"key":"8894_CR35","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/2213292","author":"Z Shou","year":"2022","unstructured":"Shou Z, Chen P, Wen H et al (2022) MOOC Dropout Prediction Based on Multidimensional Time-Series Data. Math Prob Eng. https:\/\/doi.org\/10.1155\/2022\/2213292","journal-title":"Math Prob Eng"},{"key":"8894_CR36","doi-asserted-by":"publisher","unstructured":"Wu N, Zhang L, Gao Y et al (2019) CLMS-Net: dropout prediction in MOOCs with deep learning. Proceedings of the ACM Turing Celebration Conference-China 1\u20136. https:\/\/doi.org\/10.1145\/3321408.3322848","DOI":"10.1145\/3321408.3322848"},{"key":"8894_CR37","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-5364-9_6","volume-title":"Convolutional neural networks","author":"N Ketkar","year":"2017","unstructured":"Ketkar N (2017) Convolutional neural networks. Springer International Publishing. https:\/\/doi.org\/10.1007\/978-1-4842-5364-9_6"},{"issue":"5786","key":"8894_CR38","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507. https:\/\/doi.org\/10.1126\/science.1127647","journal-title":"Science"},{"issue":"Pt B","key":"8894_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106294","volume":"151","author":"K Niu","year":"2022","unstructured":"Niu K, Guo Z, Peng X et al (2022) P-ResUnet: Segmentation of brain tissue with Purified Residual Unet. Comput Biol Med 151(Pt B):106294. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106294","journal-title":"Comput Biol Med"},{"issue":"4","key":"8894_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450520","volume":"21","author":"W Lu","year":"2021","unstructured":"Lu W, Yu R, Wang S et al (2021) Sentence Semantic Matching Based on 3D CNN for Human-Robot Language Interaction. ACM Trans Int Technol 21(4):1\u201324. https:\/\/doi.org\/10.1145\/3450520","journal-title":"ACM Trans Int Technol"},{"issue":"8","key":"8894_CR41","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 Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"issue":"1","key":"8894_CR42","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach Learn"},{"issue":"2","key":"8894_CR43","first-page":"3","volume":"1","author":"H Zhang","year":"2004","unstructured":"Zhang H (2004) The optimality of naive Bayes. Aa 1(2):3.","journal-title":"Aa"},{"key":"8894_CR44","doi-asserted-by":"crossref","unstructured":"Dey R, Salem FM. Gate-variants of gated recurrent unit (GRU) neural networks (2017) IEEE 60th international midwest symposium on circuits and systems (MWSCAS). IEEE 1597\u20131600.","DOI":"10.1109\/MWSCAS.2017.8053243"},{"issue":"1","key":"8894_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-93543-8","volume":"11","author":"F Khozeimeh","year":"2021","unstructured":"Khozeimeh F, Sharifrazi D, Izadi NH et al (2021) Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Sci Rep 11(1):1\u201318. https:\/\/doi.org\/10.1038\/s41598-021-93543-8","journal-title":"Sci Rep"},{"issue":"6","key":"8894_CR46","doi-asserted-by":"publisher","DOI":"10.1088\/1873-7005\/abb91d","volume":"52","author":"K Hasegawa","year":"2020","unstructured":"Hasegawa K, Fukami K, Murata T et al (2020) CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers. Fluid Dyn Res 52(6):065501. https:\/\/doi.org\/10.1088\/1873-7005\/abb91d","journal-title":"Fluid Dyn Res"},{"key":"8894_CR47","unstructured":"Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Adv Neural Inf Process Syst, 30."}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08894-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08894-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08894-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T15:04:52Z","timestamp":1694876692000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08894-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,8]]},"references-count":47,"journal-issue":{"issue":"30","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["8894"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08894-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,8]]},"assertion":[{"value":"27 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 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":"The authors declare no potential conflict of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}