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Advances in Intelligent Tutoring Systems

Overview of attention for book
Cover of 'Advances in Intelligent Tutoring Systems'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction: What Are Intelligent Tutoring Systems, and Why This Book?
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    Chapter 2 Modeling the Domain: An Introduction to the Expert Module
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    Chapter 3 Rule-Based Cognitive Modeling for Intelligent Tutoring Systems
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    Chapter 4 Modeling Domains and Students with Constraint-Based Modeling
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    Chapter 5 Building Intelligent Tutoring Systems for Ill-Defined Domains
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    Chapter 6 A Survey of Domain Ontology Engineering: Methods and Tools
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    Chapter 7 Modeling Tutoring Knowledge
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    Chapter 8 Decision-Making in Cognitive Tutoring Systems
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    Chapter 9 Tutorial Dialog in Natural Language
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    Chapter 10 Affective Tutors: Automatic Detection of and Response to Student Emotion
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    Chapter 11 Ontology-Based Formal Modeling of the Pedagogical World: Tutor Modeling
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    Chapter 12 Using Drawings in Knowledge Modeling and Simulation for Science Teaching
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    Chapter 13 Student Modeling
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    Chapter 14 Bayesian Student Modeling
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    Chapter 15 Open Learner Models
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    Chapter 16 Mining Data for Student Models
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    Chapter 17 Managing Learner’s Affective States in Intelligent Tutoring Systems
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    Chapter 18 Building Intelligent Tutoring Systems: An Overview
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    Chapter 19 Authoring Problem-Solving Tutors: A Comparison between ASTUS and CTAT
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    Chapter 20 Open Content Authoring Tools
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    Chapter 21 The Andes Physics Tutoring System: An Experiment in Freedom
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    Chapter 22 Advances in Intelligent Tutoring Systems
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    Chapter 23 Preserving Learners’ Privacy
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    Chapter 24 Advances in Intelligent Tutoring Systems
Overall attention for this book and its chapters
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Citations

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Readers on

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Title
Advances in Intelligent Tutoring Systems
Published by
Springer Berlin Heidelberg, September 2010
DOI 10.1007/978-3-642-14363-2
ISBNs
978-3-64-214362-5, 978-3-64-214363-2
Editors

Nkambou, Roger, Bourdeau, Jacqueline, Mizoguchi, Riichiro

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 150 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Canada 1 <1%
Italy 1 <1%
United Kingdom 1 <1%
Indonesia 1 <1%
Singapore 1 <1%
Argentina 1 <1%
Spain 1 <1%
Switzerland 1 <1%
Other 0 0%
Unknown 139 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 47 31%
Student > Master 29 19%
Researcher 19 13%
Student > Bachelor 13 9%
Student > Doctoral Student 10 7%
Other 20 13%
Unknown 12 8%
Readers by discipline Count As %
Computer Science 82 55%
Social Sciences 15 10%
Engineering 14 9%
Psychology 7 5%
Mathematics 3 2%
Other 14 9%
Unknown 15 10%