LEADER 03392nam 22005535 450 001 9910299873003321 005 20200707010533.0 010 $a3-319-67928-7 024 7 $a10.1007/978-3-319-67928-0 035 $a(CKB)4100000000881537 035 $a(DE-He213)978-3-319-67928-0 035 $a(MiAaPQ)EBC6299003 035 $a(MiAaPQ)EBC5577612 035 $a(Au-PeEL)EBL5577612 035 $a(OCoLC)1066197282 035 $a(PPN)22012504X 035 $a(EXLCZ)994100000000881537 100 $a20171027d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAI Injected e-Learning $eThe Future of Online Education /$fby Matthew Montebello 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (XIX, 86 p. 6 illus.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v745 311 $a3-319-67927-9 327 $aIntroduction -- e-Learning so far -- MOOCs, Crowdsourcing and Social Networks -- User Pro?ling and Personalisation -- Personal Learning Networks, Portfolios and Environments -- Customised e-Learning -- Looking Ahead. 330 $aThis book reviews a blend of artificial intelligence (AI) approaches that can take e-learning to the next level by adding value through customization. It investigates three methods: crowdsourcing via social networks; user profiling through machine learning techniques, and personal learning portfolios using learning analytics. Technology and education have drawn closer together over the years as they complement each other within the domain of e-learning, and different generations of online education reflect the evolution of new technologies as researcher and developers continuously seek to optimize the electronic medium to enhance the effectiveness of e-learning. Artificial intelligence (AI) for e-learning promises personalized online education through a combination of different intelligent techniques that are grounded in established learning theories while at the same time addressing a number of common e-learning issues. This book is intended for education technologists and e-learning researchers as well as for a general readership interested in the evolution of online education based on techniques like machine learning, crowdsourcing, and learner profiling that can be merged to characterize the future of personalized e-learning. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v745 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a371.3344678 700 $aMontebello$b Matthew$4aut$4http://id.loc.gov/vocabulary/relators/aut$01063951 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299873003321 996 $aAI Injected e-Learning$92535452 997 $aUNINA