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020 _a9783319679280
_9978-3-319-67928-0
024 7 _a10.1007/978-3-319-67928-0
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
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082 0 4 _a006.3
_223
100 1 _aMontebello, Matthew.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_951591
245 1 0 _aAI Injected e-Learning
_h[electronic resource] :
_bThe Future of Online Education /
_cby Matthew Montebello.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIX, 86 p. 6 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v745
505 0 _aIntroduction -- e-Learning so far -- MOOCs, Crowdsourcing and Social Networks -- User Proļ¬ling and Personalisation -- Personal Learning Networks, Portfolios and Environments -- Customised e-Learning -- Looking Ahead.
520 _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.
650 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
710 2 _aSpringerLink (Online service)
_951592
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319679273
776 0 8 _iPrinted edition:
_z9783319679297
776 0 8 _iPrinted edition:
_z9783319885131
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v745
_951593
856 4 0 _uhttps://doi.org/10.1007/978-3-319-67928-0
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c78798
_d78798