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Machine Learning Paradigms

Overview of attention for book
Cover of 'Machine Learning Paradigms'

Table of Contents

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    Book Overview
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    Chapter 1 Machine Learning Paradigms
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    Chapter 2 Using a Multi Module Model for Learning Analytics to Predict Learners’ Cognitive States and Provide Tailored Learning Pathways and Assessment
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    Chapter 3 Analytics for Student Engagement
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    Chapter 4 Assessing Self-regulation, a New Topic in Learning Analytics: Process of Information Objectification
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    Chapter 5 Learning Feedback Based on Dispositional Learning Analytics
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    Chapter 6 The Variability of the Reasons for Student Dropout in Distance Learning and the Prediction of Dropout-Prone Students
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    Chapter 7 An Architectural Perspective of Learning Analytics
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    Chapter 8 Multimodal Learning Analytics in a Laboratory Classroom
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    Chapter 9 Dashboards for Computer-Supported Collaborative Learning
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    Chapter 10 Learning Analytics in Distance and Mobile Learning for Designing Personalised Software
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    Chapter 11 Optimizing Programming Language Learning Through Student Modeling in an Adaptive Web-Based Educational Environment
Attention for Chapter 2: Using a Multi Module Model for Learning Analytics to Predict Learners’ Cognitive States and Provide Tailored Learning Pathways and Assessment
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Citations

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Readers on

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77 Mendeley
Summary Dimensions citations
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Chapter title
Using a Multi Module Model for Learning Analytics to Predict Learners’ Cognitive States and Provide Tailored Learning Pathways and Assessment
Chapter number 2
Book title
Machine Learning Paradigms
Published by
Springer, Cham, January 2020
DOI 10.1007/978-3-030-13743-4_2
Book ISBNs
978-3-03-013742-7, 978-3-03-013743-4
Authors

Christos Troussas, Akrivi Krouska, Maria Virvou, Troussas, Christos, Krouska, Akrivi, Virvou, Maria

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 77 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 13%
Student > Ph. D. Student 10 13%
Lecturer 9 12%
Researcher 7 9%
Lecturer > Senior Lecturer 4 5%
Other 13 17%
Unknown 24 31%
Readers by discipline Count As %
Computer Science 24 31%
Social Sciences 9 12%
Arts and Humanities 4 5%
Agricultural and Biological Sciences 3 4%
Economics, Econometrics and Finance 2 3%
Other 8 10%
Unknown 27 35%

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