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AI injected e-learningthe future of ...

Montebello, Matthew.

AI injected e-learningthe future of online education /

Record Type:	Electronic resources : Monograph/item
Title/Author:	AI injected e-learningby Matthew Montebello.
Reminder of title:	the future of online education /
Author:	Montebello, Matthew.
Published:	Cham :Springer International Publishing :2018.
Description:	xix, 86 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Artificial intelligenceEducational applications.
Online resource:	http://dx.doi.org/10.1007/978-3-319-67928-0
ISBN:	9783319679280$q(electronic bk.)

AI injected e-learningthe future of online education /
Montebello, Matthew.

AI injected e-learningthe future of online education /[electronic resource] :by Matthew Montebello. - Cham :Springer International Publishing :2018. - xix, 86 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.7451860-949X ;. - Studies in computational intelligence ;v. 216..

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.

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.

ISBN: 9783319679280$q(electronic bk.)

Standard No.: 10.1007/978-3-319-67928-0doiSubjects--Topical Terms:

203570
Artificial intelligence
--Educational applications.

LC Class. No.: LB1028.43

Dewey Class. No.: 371.33463

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245 1 0 $a AI injected e-learning $h [electronic resource] : $b the future of online education / $c by Matthew Montebello.
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505 0 $a 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 $a 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.
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