{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T07:29:44Z","timestamp":1782631784259,"version":"3.54.5"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T00:00:00Z","timestamp":1710374400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T00:00:00Z","timestamp":1710374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Educ Inf Technol"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s10639-024-12588-0","type":"journal-article","created":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T08:45:39Z","timestamp":1710405939000},"page":"18839-18857","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A hybrid approach for early-identification of at-risk dropout students using LSTM-DNN networks"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5878-726X","authenticated-orcid":false,"given":"Houssam","family":"El\u00a0Aouifi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0327-8249","authenticated-orcid":false,"given":"Mohamed","family":"El\u00a0Hajji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4934-2322","authenticated-orcid":false,"given":"Youssef","family":"Es-Saady","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,14]]},"reference":[{"key":"12588_CR1","doi-asserted-by":"crossref","unstructured":"Adelman, M., Haimovich, F., Ham, A., & et al. (2018). Predicting school dropout with administrative data: new evidence from guatemala and honduras. Education Economics, 26(4), 356\u2013372.","DOI":"10.1080\/09645292.2018.1433127"},{"key":"12588_CR2","doi-asserted-by":"crossref","unstructured":"Agrusti, F., Mezzini, M., & Bonavolont\u00e0, G. (2020). Deep learning approach for predicting university dropout: A case study at roma tre university. Journal of e-Learning and Knowledge Society, 16(1), 44\u201354.","DOI":"10.21125\/inted.2019.2274"},{"key":"12588_CR3","doi-asserted-by":"crossref","unstructured":"Al-Azazi, F.A., & Ghurab, M. (2023). Ann-lstm: A deep learning model for early student performance prediction in mooc. Heliyon","DOI":"10.2139\/ssrn.4335519"},{"key":"12588_CR4","doi-asserted-by":"crossref","unstructured":"Baranyi, M., Nagy, M., & Molontay, R. (2020). Interpretable deep learning for university dropout prediction. In: Proceedings of the 21st annual conference on information technology education, pp 13\u201319","DOI":"10.1145\/3368308.3415382"},{"issue":"1","key":"12588_CR5","doi-asserted-by":"publisher","first-page":"202","DOI":"10.17081\/invinno.10.1.5607","volume":"10","author":"MJS Baron","year":"2022","unstructured":"Baron, M. J. S., Sanabria, J. S. G., & Diaz, J. E. E. (2022). Deep neural network dnn applied to the analysis of student dropout. Investigaci\u00f3n e Innovaci\u00f3n en Ingenier\u00edas, 10(1), 202\u2013214.","journal-title":"Investigaci\u00f3n e Innovaci\u00f3n en Ingenier\u00edas"},{"key":"12588_CR6","doi-asserted-by":"crossref","unstructured":"Barros, T. M., Souza Neto, P. A., Silva, I., & et al. (2019). Predictive models for imbalanced data: A school dropout perspective. Education Sciences, 9(4), 275.","DOI":"10.3390\/educsci9040275"},{"issue":"2","key":"12588_CR7","doi-asserted-by":"publisher","first-page":"1068","DOI":"10.3390\/app13021068","volume":"13","author":"Z Chi","year":"2023","unstructured":"Chi, Z., Zhang, S., & Shi, L. (2023). Analysis and prediction of mooc learners\u2019 dropout behavior. Applied Sciences, 13(2), 1068.","journal-title":"Applied Sciences"},{"key":"12588_CR8","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.childyouth.2018.11.030","volume":"96","author":"JY Chung","year":"2019","unstructured":"Chung, J. Y., & Lee, S. (2019). Dropout early warning systems for high school students using machine learning. Children and Youth Services Review, 96, 346\u2013353.","journal-title":"Children and Youth Services Review"},{"key":"12588_CR9","doi-asserted-by":"crossref","unstructured":"Coppo, E.C., Caetano, R.S., de\u00a0Lima, L.M., & et\u00a0al. (2022). Student dropout prediction using 1d cnn-lstm with variational autoencoder oversampling. In: 2022 IEEE Latin American Conference on Computational Intelligence (LA-CCI), IEEE, pp 1\u20136","DOI":"10.1109\/LA-CCI54402.2022.9981340"},{"key":"12588_CR10","doi-asserted-by":"crossref","unstructured":"De Witte, K., Cabus, S., Thyssen, G., & et al. (2013). A critical review of the literature on school dropout. Educational Research Review, 10, 13\u201328.","DOI":"10.1016\/j.edurev.2013.05.002"},{"key":"12588_CR11","doi-asserted-by":"crossref","unstructured":"El Aouifi, H., El Hajji, M., Es-Saady, Y., & et al. (2021). Predicting learner\u2019s performance through video sequences viewing behavior analysis using educational data-mining. Education and Information Technologies, 26(5), 5799\u20135814.","DOI":"10.1007\/s10639-021-10512-4"},{"key":"12588_CR12","unstructured":"Fayyad, U. (2005). Knowledge discovery in databases: An overview. In: Inductive Logic Programming: 7th International Workshop, ILP-97 Prague, Czech Republic September 17\u201320, 1997 Proceedings, Springer, pp 1\u201316"},{"key":"12588_CR13","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, Ar., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing, Ieee, pp 6645\u20136649","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"12588_CR14","doi-asserted-by":"crossref","unstructured":"Hegde, V., & Prageeth, P. (2018). Higher education student dropout prediction and analysis through educational data mining. In: 2nd International Conference on Inventive Systems and Control (ICISC). IEEE, pp 694\u2013699","DOI":"10.1109\/ICISC.2018.8398887"},{"issue":"15","key":"12588_CR15","doi-asserted-by":"publisher","first-page":"3093","DOI":"10.3390\/app9153093","volume":"9","author":"S Lee","year":"2019","unstructured":"Lee, S., & Chung, J. Y. (2019). The machine learning-based dropout early warning system for improving the performance of dropout prediction. Applied Sciences, 9(15), 3093.","journal-title":"Applied Sciences"},{"key":"12588_CR16","doi-asserted-by":"crossref","unstructured":"Martins, M. V., Baptista, L., Machado, J., & et al. (2023). Multi-class phased prediction of academic performance and dropout in higher education. Applied Sciences, 13(8), 4702.","DOI":"10.3390\/app13084702"},{"key":"12588_CR17","doi-asserted-by":"crossref","unstructured":"Mimis, M., El Hajji, M., Es-saady, Y., & et al. (2019). A framework for smart academic guidance using educational data mining. Education and Information Technologies, 24, 1379\u20131393.","DOI":"10.1007\/s10639-018-9838-8"},{"key":"12588_CR18","unstructured":"Minn, S. (2020). Bkt-lstm: Efficient student modeling for knowledge tracing and student performance prediction. arXiv:2012.12218"},{"key":"12588_CR19","doi-asserted-by":"crossref","unstructured":"Nagy, M., & Molontay, R. (2018). Predicting dropout in higher education based on secondary school performance. In: IEEE 22nd international conference on intelligent engineering systems (INES). IEEE, pp 000,389\u2013000,394","DOI":"10.1109\/INES.2018.8523888"},{"key":"12588_CR20","doi-asserted-by":"crossref","unstructured":"Nascimento, R.L.Sd., Neves\u00a0Junior, R.Bd., Almeida\u00a0Neto, M.Ad., & et\u00a0al. (2018). Educational data mining: An application of regressors in predicting school dropout. In: International Conference on Machine Learning and Data Mining in Pattern Recognition. Springer, pp 246\u2013257","DOI":"10.1007\/978-3-319-96133-0_19"},{"key":"12588_CR21","doi-asserted-by":"publisher","unstructured":"Realinho, V., Machado, J., Baptista, L., & et\u00a0al. (2021). Predict students\u2019 dropout and academic success. https:\/\/doi.org\/10.5281\/zenodo.5777340","DOI":"10.5281\/zenodo.5777340"},{"key":"12588_CR22","doi-asserted-by":"crossref","unstructured":"Sorensen, L. C. (2019). \u201cbig data\u201d in educational administration: An application for predicting school dropout risk. Educational Administration Quarterly, 55(3), 404\u2013446.","DOI":"10.1177\/0013161X18799439"},{"issue":"1","key":"12588_CR23","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1080\/1360080X.2020.1739800","volume":"43","author":"PT Von Hippel","year":"2021","unstructured":"Von Hippel, P. T., & Hofflinger, A. (2021). The data revolution comes to higher education: identifying students at risk of dropout in chile. Journal of Higher Education Policy and Management, 43(1), 2\u201323.","journal-title":"Journal of Higher Education Policy and Management"},{"issue":"3","key":"12588_CR24","first-page":"115","volume":"13","author":"PA Willging","year":"2009","unstructured":"Willging, P. A., & Johnson, S. D. (2009). Factors that influence students\u2019 decision to dropout of online courses. Journal of Asynchronous Learning Networks, 13(3), 115\u2013127.","journal-title":"Journal of Asynchronous Learning Networks"},{"key":"12588_CR25","doi-asserted-by":"crossref","unstructured":"Wu, N., Zhang, L., Gao, Y., & et\u00a0al. (2019). Clms-net: dropout prediction in moocs with deep learning. In: Proceedings of the ACM Turing Celebration Conference-China, pp 1\u20136","DOI":"10.1145\/3321408.3322848"},{"key":"12588_CR26","doi-asserted-by":"crossref","unstructured":"Xiong, F., Zou, K., Liu, Z., & et\u00a0al. (2019). Predicting learning status in moocs using lstm. In: Proceedings of the ACM Turing Celebration Conference-China, pp 1\u20135","DOI":"10.1145\/3321408.3322855"},{"key":"12588_CR27","doi-asserted-by":"crossref","unstructured":"Yao, H., Wu, F., Ke, J., & et\u00a0al. (2018). Deep multi-view spatial-temporal network for taxi demand prediction. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11836"},{"key":"12588_CR28","doi-asserted-by":"crossref","unstructured":"Yu, Y., Si, X., Hu, C., & et al. (2019). A review of recurrent neural networks: Lstm cells and network architectures. Neural computation, 31(7), 1235\u20131270.","DOI":"10.1162\/neco_a_01199"},{"key":"12588_CR29","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Shao, Z., Deng, M., & et al. (2022). Mooc dropout prediction using a fusion deep model based on behaviour features. Computers and Electrical Engineering, 104(108), 409.","DOI":"10.1016\/j.compeleceng.2022.108409"}],"container-title":["Education and Information Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10639-024-12588-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10639-024-12588-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10639-024-12588-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T13:14:24Z","timestamp":1729862064000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10639-024-12588-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,14]]},"references-count":29,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["12588"],"URL":"https:\/\/doi.org\/10.1007\/s10639-024-12588-0","relation":{},"ISSN":["1360-2357","1573-7608"],"issn-type":[{"value":"1360-2357","type":"print"},{"value":"1573-7608","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,14]]},"assertion":[{"value":"26 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 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":"We declare that we have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}