Machine Learning Paradigms: Advances in Learning Analytics

✍ Scribed by Maria Virvou, Efthimios Alepis, George A. Tsihrintzis, Lakhmi C. Jain

• Publisher: Springer International Publishing
• Year: 2020
• Tongue: English
• Leaves: 230
• Series: Intelligent Systems Reference Library 158
• Edition: 1st ed.
• Category: Library

✦ 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.

✦ Table of Contents

Front Matter ....Pages i-xvi
Machine Learning Paradigms (Maria Virvou, Efthimios Alepis, George A. Tsihrintzis, Lakhmi C. Jain)....Pages 1-5
Front Matter ....Pages 7-7
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
Analytics for Student Engagement (J. M. Vytasek, A. Patzak, P. H. Winne)....Pages 23-48
Assessing Self-regulation, a New Topic in Learning Analytics: Process of Information Objectification (David Martín Santos Melgoza)....Pages 49-66
Front Matter ....Pages 67-67
Learning Feedback Based on Dispositional Learning Analytics (Dirk Tempelaar, Quan Nguyen, Bart Rienties)....Pages 69-89
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
Front Matter ....Pages 113-113
An Architectural Perspective of Learning Analytics (Arvind W. Kiwelekar, Manjushree D. Laddha, Laxman D. Netak, Sanil Gandhi)....Pages 115-130
Multimodal Learning Analytics in a Laboratory Classroom (Man Ching Esther Chan, Xavier Ochoa, David Clarke)....Pages 131-156
Dashboards for Computer-Supported Collaborative Learning (Arita L. Liu, John C. Nesbit)....Pages 157-182
Front Matter ....Pages 183-183
Learning Analytics in Distance and Mobile Learning for Designing Personalised Software (Katerina Kabassi, Efthimios Alepis)....Pages 185-203
Optimizing Programming Language Learning Through Student Modeling in an Adaptive Web-Based Educational Environment (Konstantina Chrysafiadi, Maria Virvou, Evangelos Sakkopoulos)....Pages 205-223

✦ Subjects

Engineering; Computational Intelligence; Learning and Instruction; Data Mining and Knowledge Discovery