| 000 | 03322nam a22005175i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-67928-0 | ||
| 003 | DE-He213 | ||
| 005 | 20220801220645.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 171027s2018 sz | s |||| 0|eng d | ||
| 020 |
_a9783319679280 _9978-3-319-67928-0 |
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| 024 | 7 |
_a10.1007/978-3-319-67928-0 _2doi |
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| 050 | 4 | _aQ342 | |
| 072 | 7 |
_aUYQ _2bicssc |
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_aTEC009000 _2bisacsh |
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_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. |
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| 300 |
_aXIX, 86 p. 6 illus. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aStudies in Computational Intelligence, _x1860-9503 ; _v745 |
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| 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 |
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| 650 | 0 |
_aArtificial intelligence. _93407 |
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| 650 | 1 | 4 |
_aComputational Intelligence. _97716 |
| 650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
| 710 | 2 |
_aSpringerLink (Online service) _951592 |
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| 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 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-67928-0 |
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