Data Analytics in e-Learning: Approaches and Applications

Marian Cristian Mih¿escu

ISBN 10: 3030966461 / ISBN 13: 9783030966461

Published by Springer, 2023

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book focuses on research and development aspects of building data analytics workflows that address various challenges of e-learning applications.This book represents a guideline for building a data analysis workflow from scratch. Each chapter presents a step of the entire workflow, starting from an available dataset and continuing with building interpretable models, enhancing models, and tackling aspects of evaluating engagement and usability. The related work shows that many papers have focused on machine learning usage and advancement within e-learning systems. However, limited discussions have been found on presenting a detailed complete roadmap from the raw dataset up to the engagement and usability issues. Practical examples and guidelines are provided for designing and implementing new algorithms that address specific problems or functionalities. This roadmap represents a potential resource for various advances of researchers and practitioners in educational datamining and learning analytics.

Seller Inventory # 9783030966461

Bibliographic Details

• Title: Data Analytics in e-Learning: Approaches and Applications
• Publisher: Springer
• Publication Date: 2023
• Language: English
• ISBN 10: 3030966461
• ISBN 13: 9783030966461
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 10D millimeters 235 millimeters width by 155 millimeters height by 10 millimeters depth
• Weight: 277 grams

About this title

Synopsis

This book focuses on research and development aspects of building data analytics workflows that address various challenges of e-learning applications.

This book represents a guideline for building a data analysis workflow from scratch. Each chapter presents a step of the entire workflow, starting from an available dataset and continuing with building interpretable models, enhancing models, and tackling aspects of evaluating engagement and usability. The related work shows that many papers have focused on machine learning usage and advancement within e-learning systems. However, limited discussions have been found on presenting a detailed complete roadmap from the raw dataset up to the engagement and usability issues. Practical examples and guidelines are provided for designing and implementing new algorithms that address specific problems or functionalities. This roadmap represents a potential resource for various advances of researchers and practitioners in educational datamining and learning analytics.

About the Author

Marian Cristian Mihăescu is Associate Professor at the Department of Computer Science and Information Technologies, Faculty of Automatics, Computers and Electronics, University of Craiova, Romania. He has published over 70 articles refereed journals, conference proceedings, and chapters. He is Co-founder of Tesys e-Learning system currently running at the University of Craiova, while more than 15 articles are related to its research issues. He has taught the course “Educational Data Mining” at Tallinn University at doctorate level and was Visiting Researcher at “Knowledge Discovery and Intelligent Systems” research group at the University of Cordoba during his postdoctoral program with the title “Software system for enhancing the quality of educational services offered by e-learning platforms” during 2010–2012.