AI Injected e-Learning (Record no. 78798)
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000 -LEADER | |
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fixed length control field | 03322nam a22005175i 4500 |
001 - CONTROL NUMBER | |
control field | 978-3-319-67928-0 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20220801220645.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 171027s2018 sz | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
ISBN | 9783319679280 |
-- | 978-3-319-67928-0 |
082 04 - CLASSIFICATION NUMBER | |
Call Number | 006.3 |
100 1# - AUTHOR NAME | |
Author | Montebello, Matthew. |
245 10 - TITLE STATEMENT | |
Title | AI Injected e-Learning |
Sub Title | The Future of Online Education / |
250 ## - EDITION STATEMENT | |
Edition statement | 1st ed. 2018. |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | XIX, 86 p. 6 illus. |
490 1# - SERIES STATEMENT | |
Series statement | Studies in Computational Intelligence, |
505 0# - FORMATTED CONTENTS NOTE | |
Remark 2 | Introduction -- e-Learning so far -- MOOCs, Crowdsourcing and Social Networks -- User Profiling and Personalisation -- Personal Learning Networks, Portfolios and Environments -- Customised e-Learning -- Looking Ahead. |
520 ## - SUMMARY, ETC. | |
Summary, etc | This 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. |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | https://doi.org/10.1007/978-3-319-67928-0 |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | eBooks |
264 #1 - | |
-- | Cham : |
-- | Springer International Publishing : |
-- | Imprint: Springer, |
-- | 2018. |
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-- | computer |
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-- | online resource |
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347 ## - | |
-- | text file |
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computational intelligence. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Artificial intelligence. |
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computational Intelligence. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Artificial Intelligence. |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
-- | 1860-9503 ; |
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-- | ZDB-2-ENG |
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-- | ZDB-2-SXE |
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