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Machine Learning Paradigms

Advances in Learning Analytics

  • Maria Virvou
  • Efthimios Alepis
  • George A. Tsihrintzis
  • Lakhmi C. Jain
Book

Part of the Intelligent Systems Reference Library book series (ISRL, volume 158)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Maria Virvou, Efthimios Alepis, George A. Tsihrintzis, Lakhmi C. Jain
    Pages 1-5
  3. Learning Analytics with the Purpose to Measure Student Engagement, to Quantify the Learning Experience and to Facilitate Self-Regulation

  4. Learning Analytics to Predict Student Performance

    1. Front Matter
      Pages 67-67
    2. Dirk Tempelaar, Quan Nguyen, Bart Rienties
      Pages 69-89
    3. Christos Pierrakeas, Giannis Koutsonikos, Anastasia-Dimitra Lipitakis, Sotiris Kotsiantis, Michalis Xenos, George A. Gravvanis
      Pages 91-111
  5. Learning Analytics Incorporated in Tools for Building Learning Materials and Educational Courses

    1. Front Matter
      Pages 113-113
    2. Arvind W. Kiwelekar, Manjushree D. Laddha, Laxman D. Netak, Sanil Gandhi
      Pages 115-130
    3. Man Ching Esther Chan, Xavier Ochoa, David Clarke
      Pages 131-156
    4. Arita L. Liu, John C. Nesbit
      Pages 157-182
  6. Learning Analytics as Tools to Support Learners and Educators in Synchronous and Asynchronous e-Learning

    1. Front Matter
      Pages 183-183
    2. Konstantina Chrysafiadi, Maria Virvou, Evangelos Sakkopoulos
      Pages 205-223

About this book

Introduction

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.

Keywords

Learning Analytics Mobile Learning Educational Tools Social Network Learning Big Learning Data Analytics in User Modelling

Editors and affiliations

  • Maria Virvou
    • 1
  • Efthimios Alepis
    • 2
  • George A. Tsihrintzis
    • 3
  • Lakhmi C. Jain
    • 4
  1. 1.Department of InformaticsUniversity of PiraeusPiraeusGreece
  2. 2.Department of InformaticsUniversity of PiraeusPiraeusGreece
  3. 3.University of PiraeusPireasGreece
  4. 4.Faculty of Science, Technology and MathematicsUniversity of CanberraCanberraAustralia

Bibliographic information