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Artificial Intelligence in Education

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

  • Conference proceedings
  • © 2013

Overview

Editors:
  1. H. Chad Lane

    1. Institute for Creative Technologies, University of Southern California, Playa Vista, USA

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  2. Kalina Yacef

    1. School of Information Technologies, University of Sydney, Sydney, Australia

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  3. Jack Mostow

    1. School of Computer Science, Carnegie Mellon University, Pittsburgh, USA

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  4. Philip Pavlik

    1. Department of Psychology, University of Memphis, Memphis, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 7926)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: AIED 2013.

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Table of contents (165 papers)

  1. Front Matter

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  2. Affective Computing and Engagement

    1. Embodied Affect in Tutorial Dialogue: Student Gesture and Posture

      • Joseph F. Grafsgaard, Joseph B. Wiggins, Kristy Elizabeth Boyer, Eric N. Wiebe, James C. Lester
      Pages 1-10

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

      • Nigel Bosch, Sidney D’Mello, Caitlin Mills
      Pages 11-20

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

      • Sylvie Girard, Maria Elena Chavez-Echeagaray, Javier Gonzalez-Sanchez, Yoalli Hidalgo-Pontet, Lishan Zhang, Winslow Burleson et al.
      Pages 21-30

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

      • Lakshmi S. Doddannara, Sujith M. Gowda, Ryan S. J. d Baker, Supreeth M. Gowda, Adriana M. J. B. de Carvalho
      Pages 31-40

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

      • Maria Ofelia Z. San Pedro, Ryan S. J. d. Baker, Sujith M. Gowda, Neil T. Heffernan
      Pages 41-50

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

      • Blair Lehman, Sidney D’Mello, Art Graesser
      Pages 51-60

    7. Aligning and Comparing Data on Emotions Experienced during Learning with MetaTutor

      • Jason M. Harley, François Bouchet, Roger Azevedo
      Pages 61-70

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

      • Caitlin Mills, Sidney D’Mello, Blair Lehman, Nigel Bosch, Amber Strain, Art Graesser
      Pages 71-80

  3. Learning Together

    1. Automatically Generating Discussion Questions

      • David Adamson, Divyanshu Bhartiya, Biman Gujral, Radhika Kedia, Ashudeep Singh, Carolyn P. Rosé
      Pages 81-90

    2. Identifying Localization in Peer Reviews of Argument Diagrams

      • Huy V. Nguyen, Diane J. Litman
      Pages 91-100

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

      • Roberto Martinez-Maldonado, Judy Kay, Kalina Yacef
      Pages 101-110

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

      • Kazuhisa Miwa, Hitoshi Terai, Shoma Okamoto, Ryuichi Nakaike
      Pages 111-120

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

      • Dimoklis Despotakis, Vania Dimitrova, Lydia Lau, Dhavalkumar Thakker, Antonio Ascolese, Lucia Pannese
      Pages 121-130

    6. Tangible Collaborative Learning with a Mixed-Reality Game: EarthShake

      • Nesra Yannier, Kenneth R. Koedinger, Scott E. Hudson
      Pages 131-140

  4. Student Modeling and Personalisation

    1. From a Customizable ITS to an Adaptive ITS

      • Nathalie Guin, Marie Lefevre
      Pages 141-150

    2. Class vs. Student in a Bayesian Network Student Model

      • Yutao Wang, Joseph Beck
      Pages 151-160

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

      • Sébastien Lallé, Jack Mostow, Vanda Luengo, Nathalie Guin
      Pages 161-170

    4. Individualized Bayesian Knowledge Tracing Models

      • Michael V. Yudelson, Kenneth R. Koedinger, Geoffrey J. Gordon
      Pages 171-180

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

      • Yutao Wang, Neil Heffernan
      Pages 181-188
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About this book

This book constitutes the refereed proceedings of the 16th International Conference on Artificial Intelligence in Education, AIED 2013, held in Memphis, TN, USA in July 2013. The 55 revised full papers presented together with 73 poster presentations were carefully reviewed and selected from a total of 168 submissions. The papers are arranged in sessions on student modeling and personalization, open-learner modeling, affective computing and engagement, educational data mining, learning together (collaborative learning and social computing), natural language processing, pedagogical agents, metacognition and self-regulated learning, feedback and scaffolding, designed learning activities, educational games and narrative, and outreach and scaling up.

Editors and Affiliations

  • Institute for Creative Technologies, University of Southern California, Playa Vista, USA

    H. Chad Lane

  • School of Information Technologies, University of Sydney, Sydney, Australia

    Kalina Yacef

  • School of Computer Science, Carnegie Mellon University, Pittsburgh, USA

    Jack Mostow

  • Department of Psychology, University of Memphis, Memphis, USA

    Philip Pavlik

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