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10.1038/s41598-020-76740-9 http://scihub22266oqcxt.onion/10.1038/s41598-020-76740-9 33199801!7669866!33199801     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2020 ; 10 (1): 19888 Nephropedia Template TP {{tp|p=33199801|t=2020. DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era |pdf=|usr=}}{{33199801}} gab.com Text DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33199801 #FOAvip Coronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-face teaching to universities and schools, forcing a crash course for online learning plans and technology for students and faculty. In the midst of this unprecedented crisis, video conferencing platforms (e.g., Zoom, WebEx, MS Teams) and learning management systems (LMSs), like Moodle, Blackboard and Google Classroom, are being adopted and heavily used as online learning environments (OLEs). However, as such media solely provide the platform for e-interaction, effective methods that can be used to predict the learner's behavior in the OLEs, which should be available as supportive tools to educators and metacognitive triggers to learners. Here we show, for the first time, that Deep Learning techniques can be used to handle LMS users' interaction data and form a novel predictive model, namely DeepLMS, that can forecast the quality of interaction (QoI) with LMS. Using Long Short-Term Memory (LSTM) networks, DeepLMS results in average testing Root Mean Square Error (RMSE) [Formula: see text], and average correlation coefficient between ground truth and predicted QoI values [Formula: see text] [Formula: see text], when tested on QoI data from one database pre- and two ones during-Covid-19 pandemic. DeepLMS personalized QoI forecasting scaffolds user's online learning engagement and provides educators with an evaluation path, additionally to the content-related assessment, enriching the overall view on the learners' motivation and participation in the learning process. Twit Text FOAVip DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33199801 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33199801 #FOAvip English Wikipedia <ref>{{Cite pmid|33199801}}</ref> DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era #MMPMID33199801 Dias SB; Hadjileontiadou SJ; Diniz J; Hadjileontiadis LJ Sci Rep 2020[Nov]; 10 (1): 19888 PMID33199801show ga Coronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-face teaching to universities and schools, forcing a crash course for online learning plans and technology for students and faculty. In the midst of this unprecedented crisis, video conferencing platforms (e.g., Zoom, WebEx, MS Teams) and learning management systems (LMSs), like Moodle, Blackboard and Google Classroom, are being adopted and heavily used as online learning environments (OLEs). However, as such media solely provide the platform for e-interaction, effective methods that can be used to predict the learner's behavior in the OLEs, which should be available as supportive tools to educators and metacognitive triggers to learners. Here we show, for the first time, that Deep Learning techniques can be used to handle LMS users' interaction data and form a novel predictive model, namely DeepLMS, that can forecast the quality of interaction (QoI) with LMS. Using Long Short-Term Memory (LSTM) networks, DeepLMS results in average testing Root Mean Square Error (RMSE) [Formula: see text], and average correlation coefficient between ground truth and predicted QoI values [Formula: see text] [Formula: see text], when tested on QoI data from one database pre- and two ones during-Covid-19 pandemic. DeepLMS personalized QoI forecasting scaffolds user's online learning engagement and provides educators with an evaluation path, additionally to the content-related assessment, enriching the overall view on the learners' motivation and participation in the learning process. |*Deep Learning[MESH] |*Software[MESH] |Adolescent[MESH] |Adult[MESH] |COVID-19/*epidemiology[MESH] |Computer-Assisted Instruction/methods/*standards[MESH] |Education, Professional/standards[MESH] |Humans[MESH] |Middle Aged[MESH] |Teleworking/standards[MESH]DeepLMS: a deep learning predictive model for supporting online learni(epmc).pdf DeepDyve Pubget Overpricing