Artificial Intelligence in Education

16th International Conference, AIED 2013, Memphis, TN, USA, July 9-13, 2013. Proceedings

Editors:

ISBN: 978-3-642-39111-8 (Print) 978-3-642-39112-5 (Online)

Table of contents (165 chapters)

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  1. Front Matter

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  2. Affective Computing and Engagement

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      Pages 1-10

      Embodied Affect in Tutorial Dialogue: Student Gesture and Posture

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      Pages 11-20

      What Emotions Do Novices Experience during Their First Computer Programming Learning Session?

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      Pages 21-30

      Defining the Behavior of an Affective Learning Companion in the Affective Meta-tutor Project

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      Pages 31-40

      Exploring the Relationships between Design, Students’ Affective States, and Disengaged Behaviors within an ITS

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      Pages 41-50

      Towards an Understanding of Affect and Knowledge from Student Interaction with an Intelligent Tutoring System

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      Pages 51-60

      Who Benefits from Confusion Induction during Learning? An Individual Differences Cluster Analysis

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      Pages 61-70

      Aligning and Comparing Data on Emotions Experienced during Learning with MetaTutor

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      Pages 71-80

      What Makes Learning Fun? Exploring the Influence of Choice and Difficulty on Mind Wandering and Engagement during Learning

  3. Learning Together

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      Pages 81-90

      Automatically Generating Discussion Questions

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      Pages 91-100

      Identifying Localization in Peer Reviews of Argument Diagrams

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      Pages 101-110

      An Automatic Approach for Mining Patterns of Collaboration around an Interactive Tabletop

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      Pages 111-120

      A Learning Environment That Combines Problem-Posing and Problem-Solving Activities

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      Pages 121-130

      ViewS in User Generated Content for Enriching Learning Environments: A Semantic Sensing Approach

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      Pages 131-140

      Tangible Collaborative Learning with a Mixed-Reality Game: EarthShake

  4. Student Modeling and Personalisation

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      Pages 141-150

      From a Customizable ITS to an Adaptive ITS

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      Pages 151-160

      Class vs. Student in a Bayesian Network Student Model

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      Pages 161-170

      Comparing Student Models in Different Formalisms by Predicting Their Impact on Help Success

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      Pages 171-180

      Individualized Bayesian Knowledge Tracing Models

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      Pages 181-188

      Extending Knowledge Tracing to Allow Partial Credit: Using Continuous versus Binary Nodes

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      Pages 189-198

      Using Learner Modeling to Determine Effective Conditions of Learning for Optimal Transfer

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