Data analytics in e-learning : approaches and applications / Marian Cristian Mihăescu, editor.
Mihăescu, Marian Cristian, editor.
2022
LB1028.43 .D38 2022eb
Available Online

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Title Data analytics in e-learning : approaches and applications / Marian Cristian Mihăescu, editor.
ISBN 9783030966447 (electronic bk.)
3030966445 (electronic bk.)
9783030966430
3030966437
DOI https://doi.org/10.1007/978-3-030-96644-7
Published Cham : Springer, [2022]
Copyright ©2022
Language English
Description 1 online resource (vii, 165 pages : illustrations (some color)).
Item Number 10.1007/978-3-030-96644-7 doi
Call Number LB1028.43 .D38 2022eb
Dewey Decimal Classification 371.33/4
Summary 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 data mining and learning analytics.
Bibliography, etc. Note References.
Includes bibliographical references.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed April 1, 2022).
Added Author Mihăescu, Marian Cristian, editor.
Series Intelligent systems reference library ; v. 220. 1868-4408
Available in Other Form Print version: 9783030966430
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
Introduction to Data Analytics in e-Learning
Public Datasets and Data Sources for Educational Data Mining
Building Data Analysis Workflows that Provide Personalized Recommendations for Students
Building Interpretable Machine Learning Models with Decision Trees
Enhancing Machine Learning Models by Augmenting New Functionalities
Increasing Engagement in e-Learning Systems
Usability Evaluation Roadmap for e-Learning Systems
Developing new algorithms that suite specific application requirements.

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