{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:14:34Z","timestamp":1761808474586,"version":"3.41.2"},"reference-count":33,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T00:00:00Z","timestamp":1631491200000},"content-version":"vor","delay-in-days":255,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Reading and writing are the foundations of English learning as well as an important method of instruction. With the advancement of network technology and the onset of the information age, an increasing number of students have lost interest in traditional English reading and writing instruction in the classroom. Flipped classrooms have emerged as a result of this situation and have become the focus of research in one fell swoop. As a result, flipped classroom research at home and abroad has primarily focused on the theory and practical application of flipped classrooms, and flipped classroom application practice is primarily based on the overall classroom, with few separate discussions on the effects of flipped classroom students\u2019 self\u2010learning. As a result, we developed a recurrent neural network\u2010based intelligent assisted learning algorithm for English flipped classrooms. There are two main characteristics of the model. First, it is a gated recurrent unit based on a variant structure of the recurrent neural network. The double\u2010gating mechanism fully considers the context and selects memory through weight assignment, and on this basis, it integrates the novel LeakyReLU function to improve the model\u2019s training convergence efficiency. Second, by overcoming time\u2010consuming problems in the medium, the adoption of the connection sequence classification algorithm eliminates the need for prior alignment of speech and text data, resulting in a direct boost in model training speed. The experimental results show that in the English flipped classroom\u2019s intelligent learning mode, students explore and discover knowledge independently, their enthusiasm and interest in learning are greatly increased, and the flipped classroom\u2019s teaching effect is greatly improved.<\/jats:p>","DOI":"10.1155\/2021\/8020461","type":"journal-article","created":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T18:53:59Z","timestamp":1631559239000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Intelligent Learning Algorithm for English Flipped Classroom Based on Recurrent Neural Network"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8891-519X","authenticated-orcid":false,"given":"Qi","family":"Shan","sequence":"first","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,9,13]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.52810\/TC.2021.100020"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40692-018-0117-x"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1080\/09585176.2018.1447306"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2018.07.021"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2018.09.013"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.edurev.2020.100314"},{"volume-title":"Narrative Writing Intervention Plan: Analysis of Students\u2019 Literacy Learning Needs","year":"2017","author":"Amalia N.","key":"e_1_2_8_7_2"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/socsci9080134"},{"key":"e_1_2_8_9_2","doi-asserted-by":"publisher","DOI":"10.52810\/TC.2021.100024"},{"key":"e_1_2_8_10_2","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6963-11-198"},{"key":"e_1_2_8_11_2","doi-asserted-by":"publisher","DOI":"10.1080\/09588221.2019.1584117"},{"key":"e_1_2_8_12_2","doi-asserted-by":"publisher","DOI":"10.3991\/ijet.v14i09.10348"},{"key":"e_1_2_8_13_2","doi-asserted-by":"publisher","DOI":"10.12973\/iji.2018.11226a"},{"key":"e_1_2_8_14_2","first-page":"1","volume-title":"Education and Information Technologies","author":"Karabatak S.","year":"2019"},{"key":"e_1_2_8_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcomdis.2011.01.001"},{"key":"e_1_2_8_16_2","doi-asserted-by":"publisher","DOI":"10.1017\/S1366728918000287"},{"key":"e_1_2_8_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cogdev.2020.100949"},{"key":"e_1_2_8_18_2","doi-asserted-by":"crossref","unstructured":"MitraV. SivaramanG. BartelsC. NamH. WangW. Espy-WilsonC. VergyriD. andFrancoH. Joint modeling of articulatory and acoustic spaces for continuous speech recognition tasks 2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) 2017 March New Orleans LA USA 5205\u20135209.","DOI":"10.1109\/ICASSP.2017.7953149"},{"key":"e_1_2_8_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-31372-2_15"},{"key":"e_1_2_8_20_2","doi-asserted-by":"crossref","unstructured":"AudhkhasiK. KingsburyB. RamabhadranB. SaonG. andPichenyM. Building competitive direct acoustics-to-word models for English conversational speech recognition 2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) 2018 April Calgary AB Canada 4759\u20134763.","DOI":"10.1109\/ICASSP.2018.8461935"},{"key":"e_1_2_8_21_2","doi-asserted-by":"crossref","unstructured":"ZengZ. KhassanovY. PhamV. T. XuH. ChngE. S. andLiH. On the end-to-end solution to Mandarin-English code-switching speech recognition 2018 http:\/\/arxiv.org\/abs\/1811.00241.","DOI":"10.21437\/Interspeech.2019-1429"},{"key":"e_1_2_8_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCPMT.2021.3102891"},{"key":"e_1_2_8_23_2","doi-asserted-by":"crossref","unstructured":"ParthibanR. EzhilarasiR. andSaravananD. Optical character recognition for English handwritten text using recurrent neural network In 2020 International Conference on System Computation Automation and Networking (ICSCAN) 2020 July Pondicherry India 1\u20135.","DOI":"10.1109\/ICSCAN49426.2020.9262379"},{"key":"e_1_2_8_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-019-01293-2"},{"key":"e_1_2_8_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12243-019-00731-9"},{"key":"e_1_2_8_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02572-0"},{"key":"e_1_2_8_27_2","doi-asserted-by":"publisher","DOI":"10.1002\/bmb.20992"},{"key":"e_1_2_8_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compedu.2019.02.011"},{"key":"e_1_2_8_29_2","doi-asserted-by":"publisher","DOI":"10.5688\/ajpe6922"},{"key":"e_1_2_8_30_2","doi-asserted-by":"publisher","DOI":"10.1177\/0145445518764343"},{"key":"e_1_2_8_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2021.3074469"},{"key":"e_1_2_8_32_2","doi-asserted-by":"publisher","DOI":"10.52810\/TPRIS.2021.100019"},{"key":"e_1_2_8_33_2","doi-asserted-by":"publisher","DOI":"10.52810\/TIOT.2021.100031"}],"container-title":["Wireless Communications and Mobile Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2021\/8020461.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2021\/8020461.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/8020461","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T14:01:32Z","timestamp":1723039292000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/8020461"}},"subtitle":[],"editor":[{"given":"Yuanpeng","family":"Zhang","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/8020461"],"URL":"https:\/\/doi.org\/10.1155\/2021\/8020461","archive":["Portico"],"relation":{},"ISSN":["1530-8669","1530-8677"],"issn-type":[{"type":"print","value":"1530-8669"},{"type":"electronic","value":"1530-8677"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-07-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"8020461"}}