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10.1007/978-3-030-52237-7_49 http://scihub22266oqcxt.onion/10.1007/978-3-030-52237-7_49 C7334161!7334161 !C7334161     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\C7334161 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Artificial+Intelligence+in+Education 2020 ; 12163 (ä): 610-22 Nephropedia Template TP {{tp|p=C7334161 |t=2020. Exploring Automated Question Answering Methods for Teaching Assistance |pdf=|usr=}}{{C7334161 }} gab.com Text Exploring Automated Question Answering Methods for Teaching Assistance JO: Artificial Intelligence in Education http://www.kidney.de/pget.php?&tw=1&pmid=C7334161 #FOAvip One important aspect of learning is through verbal interactions with teachers or teaching assistants (TAs), which requires significant effort and puts a heavy burden on teachers. Artificial intelligence has the potential to reduce their burden by automatically addressing the routine part of this interaction, which will free them up to focus on more important aspects of learning. We explore the use of automated question answering methods to power virtual TAs in online course discussion forums, which are heavily relied on during the COVID-19 pandemic as classes transition online. First, we focus on answering frequent and repetitive logistical questions and adopt a question answering framework that consists of two steps: retrieving relevant documents from a repository and extracting answers from retrieved documents. The document repository consists of course materials that contain information on course logistics, e.g., the syllabus, lecture slides, course emails, and prior discussion forum posts. This question answering framework can help virtual TAs decide whether a question is answerable and how to answer it. Second, we analyze the timing of student posts in discussion threads and develop a classifier to predict the timing of follow-up posts. This classifier can help virtual TAs decide whether to respond to a question and when to do so. We conduct experiments on data collected from an introductory physics course and discuss both the utility and limitations of our approach . Twit Text FOAVip Exploring Automated Question Answering Methods for Teaching Assistance ????Artificial Intelligence in Education http://www.kidney.de/pget.php?&tw=1&pmid=C7334161 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=C7334161 #FOAvip English Wikipedia <ref>{{Cite pmid|C7334161 }}</ref> Exploring Automated Question Answering Methods for Teaching Assistance #MMPMIDC7334161 Zylich B ; Viola A ; Toggerson B ; Al-Hariri L ; Lan A Artificial Intelligence in Education 2020[Jun]; 12163 (ä): 610-22 PMIDC7334161 show ga One important aspect of learning is through verbal interactions with teachers or teaching assistants (TAs), which requires significant effort and puts a heavy burden on teachers. Artificial intelligence has the potential to reduce their burden by automatically addressing the routine part of this interaction, which will free them up to focus on more important aspects of learning. We explore the use of automated question answering methods to power virtual TAs in online course discussion forums, which are heavily relied on during the COVID-19 pandemic as classes transition online. First, we focus on answering frequent and repetitive logistical questions and adopt a question answering framework that consists of two steps: retrieving relevant documents from a repository and extracting answers from retrieved documents. The document repository consists of course materials that contain information on course logistics, e.g., the syllabus, lecture slides, course emails, and prior discussion forum posts. This question answering framework can help virtual TAs decide whether a question is answerable and how to answer it. Second, we analyze the timing of student posts in discussion threads and develop a classifier to predict the timing of follow-up posts. This classifier can help virtual TAs decide whether to respond to a question and when to do so. We conduct experiments on data collected from an introductory physics course and discuss both the utility and limitations of our approach . äExploring Automated Question Answering Methods for Teaching Assistance(epmc).pdf DeepDyve Pubget Overpricing