Machine Learning Paradigms: Advances in Learning Analytics: 158 (Intelligent Systems Reference Library) - Tapa dura

9783030137427: Machine Learning Paradigms: Advances in Learning Analytics: 158 (Intelligent Systems Reference Library)

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ISBN 10: 3030137422 ISBN 13: 9783030137427

Editorial: Springer, 2019

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This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators' and learners' data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including:

• Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation;

• Using learning analytics to predict student performance;

• Using learning analytics to create learning materials and educational courses; and

• Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning.

The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.

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This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators and learners data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including:

Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation;

Using learning analytics to predict student performance;

Using learning analytics to create learning materials and educational courses; and

Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning.

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EditorialSpringer
A�o de publicaci�n2019
ISBN 10 3030137422
ISBN 13 9783030137427
Encuadernaci�nTapa dura
IdiomaIngl�s
N�mero de edici�n1
N�mero de p�ginas240
EditorVirvou Maria, Alepis Efthimios, Tsihrintzis George A.
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Machine Learning Paradigms. Advances in Learning Analytics.

Virvou, Maria; Alepis, Efthimios; Tsihrintzis, George A.; Jain, Lakhmi C. (Eds.)

Publicado por Cham, Springer., 2020

ISBN 10: 3030137422 ISBN 13: 9783030137427

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Librería: Universit�tsbuchhandlung Herta Hold GmbH, Berlin, Alemania

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XVI, 223 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband besto�en, daher M�ngelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Intelligent Systems Reference Library. Volume 158. Sprache: Englisch. N� de ref. del art�culo: 34375AB

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Machine Learning Paradigms

Virvou, Maria|Alepis, Efthimios|Tsihrintzis, George A.|Jain, Lakhmi C.

Publicado por Springer International Publishing, 2019

ISBN 10: 3030137422 ISBN 13: 9783030137427

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Gebunden. Condici�n: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents recent machine learning paradigms and advances in learning analytics Provides concise coverage from the vantage point of a newcomer, but will also appeal to experts/researchers in learning analytics Features an extended list of biblio. N� de ref. del art�culo: 267123757

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Machine Learning Paradigms

Maria Virvou

Publicado por Springer International Publishing Mrz 2019, 2019

ISBN 10: 3030137422 ISBN 13: 9783030137427

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Buch. Condici�n: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators' and learners' data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including:- Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation;- Using learning analytics to predict student performance;- Using learning analytics to create learning materials and educational courses; and- Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest. 240 pp. Englisch. N� de ref. del art�culo: 9783030137427

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Machine Learning Paradigms : Advances in Learning Analytics

Maria Virvou

Publicado por Springer International Publishing, 2019

ISBN 10: 3030137422 ISBN 13: 9783030137427

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Librería: AHA-BUCH GmbH, Einbeck, Alemania

Calificaci�n del vendedor: 5 de 5 estrellas

Buch. Condici�n: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators' and learners' data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including:- Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation;- Using learning analytics to predict student performance;- Using learning analytics to create learning materials and educational courses; and- Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest. N� de ref. del art�culo: 9783030137427

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Machine Learning Paradigms: Advances in Learning Analytics (Intelligent Systems Reference Library, 158)

Publicado por Springer, 2019

ISBN 10: 3030137422 ISBN 13: 9783030137427

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Condici�n: New. In. N� de ref. del art�culo: ria9783030137427_new

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Machine Learning Paradigms

Maria Virvou

Publicado por Springer International Publishing, Springer International Publishing M�r 2019, 2019

ISBN 10: 3030137422 ISBN 13: 9783030137427

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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania

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Buch. Condici�n: Neu. Neuware -This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators� and learners� data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including:� Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation;� Using learning analytics to predict student performance;� Using learning analytics to create learning materials and educational courses; and� Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning.The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 240 pp. Englisch. N� de ref. del art�culo: 9783030137427

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