Machine Learning Paradigms : Advances in Learning Analytics

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ISBN 10: 3030137422 / ISBN 13: 9783030137427

Published by Springer, 2019

About this Item

Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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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Bibliographic Details

• Title: Machine Learning Paradigms : Advances in Learning Analytics
• Publisher: Springer
• Publication Date: 2019
• Language: English
• ISBN 10: 3030137422
• ISBN 13: 9783030137427
• Binding: Hardcover
• Condition: Hervorragend
• Seller catalogs:
  • Bücher

About this title

Synopsis

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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