{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T14:29:53Z","timestamp":1777904993400,"version":"3.51.4"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61907011"],"award-info":[{"award-number":["61907011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Technology Research and Development Program Foundation of Henan","award":["202102310314"],"award-info":[{"award-number":["202102310314"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3045157","type":"journal-article","created":{"date-parts":[[2020,12,17]],"date-time":"2020-12-17T02:36:17Z","timestamp":1608172577000},"page":"225324-225335","source":"Crossref","is-referenced-by-count":60,"title":["MOOC Dropout Prediction Using FWTS-CNN Model Based on Fused Feature Weighting and Time Series"],"prefix":"10.1109","volume":"8","author":[{"given":"Yafeng","family":"Zheng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3539-202X","authenticated-orcid":false,"given":"Zhanghao","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Yihang","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7126-2994","authenticated-orcid":false,"given":"Qian","family":"Fu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2009.06.010"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2006.877949"},{"key":"ref33","first-page":"1","article-title":"Stock price prediction using convolutional neural networks on a multivariate timeseries","author":"mehtab","year":"2020","journal-title":"Proc Nat Conf Mach Learn Artif Intell"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2018.03.098"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93846-2_66"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3027385.3027387"},{"key":"ref37","first-page":"1265","article-title":"Comparative experiments on sentiment classification for online product reviews","author":"cui","year":"2006","journal-title":"Proc Conf DBLP"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.26599\/TST.2019.9010013"},{"key":"ref35","first-page":"527","article-title":"A nonlinear state space model for identifying at-risk students in open online courses","author":"wang","year":"2016","journal-title":"Proc Int Conf Educ Data Mining"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s11600-019-00330-1"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4102"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2011.11.002"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.promfg.2020.01.256"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICETECH.2015.7275025"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93846-2_70"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2015.174"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-018-3581-3"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ITME.2019.00100"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3316551.3316557"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1181"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40763-5_31"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2881275"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2567948.2580054"},{"key":"ref27","first-page":"492","article-title":"Early prediction of course grades: Models and feature selection","author":"li","year":"2018","journal-title":"Proc 10th Int Conf Educational Data Mining (EDM)"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.19173\/irrodl.v15i1.1651"},{"key":"ref6","first-page":"132","article-title":"Analysis and thinking of MOOC’s high Dropout rate","volume":"22","author":"li","year":"2015","journal-title":"China Educational Technique & Equipment"},{"key":"ref29","first-page":"376","article-title":"Acting the same differently: A cross-course comparison of user Behavior in MOOCs","author":"gelman","year":"2016","journal-title":"Proc Int Conf Educ Data Mining"},{"key":"ref5","first-page":"95","article-title":"Dropout: MOOC participants’ perspective","author":"parslow","year":"2014","journal-title":"Proc of EMOOCs2014"},{"key":"ref8","first-page":"1","article-title":"Dropout prediction in MOOCs using learner activity features","volume":"37","author":"halawa","year":"2014","journal-title":"eLearning Papers"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.19173\/irrodl.v15i5.1853"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2015.12.007"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2015.2504239"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-48978-9_12"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2872961"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4111"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4109"},{"key":"ref42","first-page":"85","article-title":"Research on machine learning algorithms based on the background of big data","volume":"3","author":"chen","year":"2020","journal-title":"Computer Products & Circulation"},{"key":"ref24","first-page":"583","article-title":"Analytical Analysis of Learners’ Dropout Rate with Data Mining Techniques","author":"goel","year":"2018","journal-title":"Proc ICET"},{"key":"ref41","first-page":"849","article-title":"One-against-all multi-class SVM classification using reliability measures","author":"liu","year":"2005","journal-title":"Proc IEEE Int Joint Conf Neural Netw"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCSE.2016.7581554"},{"key":"ref44","first-page":"893","article-title":"Deep learning-based dropout rate prediction method for MOOCs","volume":"41","author":"sun","year":"2019","journal-title":"Computing in Science & Eng"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/8404653"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3126973.3126990"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2015.7218617"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09296213.pdf?arnumber=9296213","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:57:13Z","timestamp":1639771033000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9296213\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3045157","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}