Machine Learning Paradigms

1. Front Matter

   Pages i-xvi

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2. Machine Learning Paradigms

   • 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

   1. Front Matter

      Pages 7-7

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   2. Using a Multi Module Model for Learning Analytics to Predict Learners’ Cognitive States and Provide Tailored Learning Pathways and Assessment

      • Christos Troussas, Akrivi Krouska, Maria Virvou

      Pages 9-22

   3. Analytics for Student Engagement

      • J. M. Vytasek, A. Patzak, P. H. Winne

      Pages 23-48

   4. Assessing Self-regulation, a New Topic in Learning Analytics: Process of Information Objectification

      • David Martín Santos Melgoza

      Pages 49-66

4. Learning Analytics to Predict Student Performance

   1. Front Matter

      Pages 67-67

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   2. Learning Feedback Based on Dispositional Learning Analytics

      • Dirk Tempelaar, Quan Nguyen, Bart Rienties

      Pages 69-89

   3. The Variability of the Reasons for Student Dropout in Distance Learning and the Prediction of Dropout-Prone Students

      • 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

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   2. An Architectural Perspective of Learning Analytics

      • Arvind W. Kiwelekar, Manjushree D. Laddha, Laxman D. Netak, Sanil Gandhi

      Pages 115-130

   3. Multimodal Learning Analytics in a Laboratory Classroom

      • Man Ching Esther Chan, Xavier Ochoa, David Clarke

      Pages 131-156

   4. Dashboards for Computer-Supported Collaborative Learning

      • 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

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   2. Learning Analytics in Distance and Mobile Learning for Designing Personalised Software

      • Katerina Kabassi, Efthimios Alepis

      Pages 185-203

   3. Optimizing Programming Language Learning Through Student Modeling in an Adaptive Web-Based Educational Environment

      • Konstantina Chrysafiadi, Maria Virvou, Evangelos Sakkopoulos

      Pages 205-223

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.

• Learning Analytics
• Mobile Learning
• Educational Tools
• Social Network Learning
• Big Learning Data
• Analytics in User Modelling
• learning and instruction

“The book … exposes its readers to the full spectrum of related research problems and progress. … Each chapter is carefully written to be self-contained and complete. … Professors, graduate students and researchers in Learning Analytics, will find this book as a valuable resource for conducting their research. … I congratulate the Editors for their outstanding work. I consider their book as an important addition to the Learning Analytics literature and I provide my highest and unreserved recommendation to it.” (Ioannis Hatzilygeroudis, Intelligent Decision Technologies, Vol. 15 (2), 2021)

• Department of Informatics, University of Piraeus, Piraeus, Greece

  Maria Virvou, Efthimios Alepis

• University of Piraeus, Pireas, Greece

  George A. Tsihrintzis

• Faculty of Science, Technology and Mathematics, University of Canberra, Canberra, Australia

  Lakhmi C. Jain

Policies and ethics