Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Title
Data analytics in e-learning : approaches and applications / Marian Cristian Mihăescu, editor.
ISBN
9783030966447 (electronic bk.)
3030966445 (electronic bk.)
9783030966430
3030966437
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).
Series
Intelligent systems reference library ; v. 220. 1868-4408
Available in Other Form
Print version: 9783030966430
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.