Description

Book 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 data mining and learning analytics.




Table of Contents

Introduction to Data Analytics in e-Learning


Data Analytics in e-Learning: Approaches and

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A Hardback by Marian Cristian Mihăescu

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    View other formats and editions of Data Analytics in e-Learning: Approaches and by Marian Cristian Mihăescu

    Publisher: Springer Nature Switzerland AG
    Publication Date: 23/03/2022
    ISBN13: 9783030966430, 978-3030966430
    ISBN10: 3030966437

    Description

    Book 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 data mining and learning analytics.




    Table of Contents

    Introduction to Data Analytics in e-Learning


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