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Advances in Intelligent Tutoring Systems

  • Book
  • © 2010

Overview

Editors:
  1. Roger Nkambou

    1. University of Quebec, Montreal, Canada

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    You can also search for this editor in PubMed   Google Scholar

  2. Jacqueline Bourdeau

    1. Télé-université, UQAM, Montreal, Canada

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    You can also search for this editor in PubMed   Google Scholar

  3. Riichiro Mizoguchi

    1. Osaka University, Osaka, Japan

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    You can also search for this editor in PubMed   Google Scholar

  • Presents recent results in intelligent tutoring systems
  • Written by leading experts in this field
  • State-of-the-Art book

Part of the book series: Studies in Computational Intelligence (SCI, volume 308)

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Table of contents (24 chapters)

  1. Front Matter

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  2. Introduction: What Are Intelligent Tutoring Systems, and Why This Book?

    1. Introduction: What Are Intelligent Tutoring Systems, and Why This Book?

      • Roger Nkambou, Jacqueline Bourdeau, Riichiro Mizoguchi
      Pages 1-12

  3. Part I: Domain Modeling

    1. Front Matter

      Pages 13-13
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    2. Modeling the Domain: An Introduction to the Expert Module

      • Roger Nkambou
      Pages 15-32

    3. Rule-Based Cognitive Modeling for Intelligent Tutoring Systems

      • Vincent Aleven
      Pages 33-62

    4. Modeling Domains and Students with Constraint-Based Modeling

      • Antonija Mitrovic
      Pages 63-80

    5. Building Intelligent Tutoring Systems for Ill-Defined Domains

      • Philippe Fournier-Viger, Roger Nkambou, Engelbert Mephu Nguifo
      Pages 81-101

    6. A Survey of Domain Ontology Engineering: Methods and Tools

      • Amal Zouaq, Roger Nkambou
      Pages 103-119

  4. Part II: Tutor Modeling and Learning Environments

    1. Front Matter

      Pages 121-121
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    2. Modeling Tutoring Knowledge

      • Jacqueline Bourdeau, Monique Grandbastien
      Pages 123-143

    3. Decision-Making in Cognitive Tutoring Systems

      • Daniel Dubois, Roger Nkambou, Jean-François Quintal, François Savard
      Pages 145-179

    4. Tutorial Dialog in Natural Language

      • Andrew M. Olney, Arthur C. Graesser, Natalie K. Person
      Pages 181-206

    5. Affective Tutors: Automatic Detection of and Response to Student Emotion

      • Beverly Park Woolf, Ivon Arroyo, David Cooper, Winslow Burleson, Kasia Muldner
      Pages 207-227

    6. Ontology-Based Formal Modeling of the Pedagogical World: Tutor Modeling

      • Riichiro Mizoguchi, Yusuke Hayashi, Jacqueline Bourdeau
      Pages 229-247

    7. Using Drawings in Knowledge Modeling and Simulation for Science Teaching

      • Wouter R. van Joolingen, Lars Bollen, Frank A. J. Leenaars
      Pages 249-264

  5. Part III: Student Modeling

    1. Front Matter

      Pages 265-265
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    2. Student Modeling

      • Beverly Park Woolf
      Pages 267-279

    3. Bayesian Student Modeling

      • Cristina Conati
      Pages 281-299

    4. Open Learner Models

      • Susan Bull, Judy Kay
      Pages 301-322

    5. Mining Data for Student Models

      • Ryan S. J. d. Baker
      Pages 323-337
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Keywords

About this book

May the Forcing Functions be with You: The Stimulating World of AIED and ITS Research It is my pleasure to write the foreword for Advances in Intelligent Tutoring S- tems. This collection, with contributions from leading researchers in the field of artificial intelligence in education (AIED), constitutes an overview of the many challenging research problems that must be solved in order to build a truly intel- gent tutoring system (ITS). The book not only describes some of the approaches and techniques that have been explored to meet these challenges, but also some of the systems that have actually been built and deployed in this effort. As discussed in the Introduction (Chapter 1), the terms “AIED” and “ITS” are often used int- changeably, and there is a large overlap in the researchers devoted to exploring this common field. In this foreword, I will use the term “AIED” to refer to the - search area, and the term “ITS” to refer to the particular kind of system that AIED researchers build. It has often been said that AIED is “AI-complete” in that to produce a tutoring system as sophisticated and effective as a human tutor requires solving the entire gamut of artificial intelligence research (AI) problems.

Editors and Affiliations

  • University of Quebec, Montreal, Canada

    Roger Nkambou

  • Télé-université, UQAM, Montreal, Canada

    Jacqueline Bourdeau

  • Osaka University, Osaka, Japan

    Riichiro Mizoguchi

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