Advances in Intelligent Tutoring Systems

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

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.

• Artificial Intelligence in Education
• Computational Intelligence
• E-learning
• Emotion
• Intellignet Tutoring
• Simulation
• knowledge
• learning
• modeling
• natural language
• ontology

• University of Quebec, Montreal, Canada

  Roger Nkambou

• Télé-université, UQAM, Montreal, Canada

  Jacqueline Bourdeau

• Osaka University, Osaka, Japan

  Riichiro Mizoguchi

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