Intelligent Tutoring Systems

1. Front Matter

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2. Affect

   1. Sensor-Free Affect Detection for a Simulation-Based Science Inquiry Learning Environment

      • Luc Paquette, Ryan S. J. D. Baker, Michael A. Sao Pedro, Janice D. Gobert, Lisa Rossi, Adam Nakama et al.

      Pages 1-10

   2. Towards Automatically Detecting Whether Student Is in Flow

      • Po-Ming Lee, Sin-Yu Jheng, Tzu-Chien Hsiao

      Pages 11-18

   3. To Quit or Not to Quit: Predicting Future Behavioral Disengagement from Reading Patterns

      • Caitlin Mills, Nigel Bosch, Art Graesser, Sidney D’Mello

      Pages 19-28

   4. Predicting Affect from Gaze Data during Interaction with an Intelligent Tutoring System

      • Natasha Jaques, Cristina Conati, Jason M. Harley, Roger Azevedo

      Pages 29-38

   5. It’s Written on Your Face: Detecting Affective States from Facial Expressions while Learning Computer Programming

      • Nigel Bosch, Yuxuan Chen, Sidney D’Mello

      Pages 39-44

   6. Impact of Agent Role on Confusion Induction and Learning

      • Blair Lehman, Arthur Graesser

      Pages 45-54

3. Multimodality and Metacognition

   1. Automated Physiological-Based Detection of Mind Wandering during Learning

      • Nathaniel Blanchard, Robert Bixler, Tera Joyce, Sidney D’Mello

      Pages 55-60

   2. Knowledge Construction with Pseudo-haptics

      • Akihiro Kashihara, Go Shiota

      Pages 61-68

   3. A Tool for Integrating Log and Video Data for Exploratory Analysis and Model Generation

      • Victor Girotto, Elissa Thomas, Cecil Lozano, Kasia Muldner, Winslow Burleson, Erin Walker

      Pages 69-74

   4. Virtual Environment for Monitoring Emotional Behaviour in Driving

      • Claude Frasson, Pierre Olivier Brosseau, Thi Hong Dung Tran

      Pages 75-83

   5. The Affective Meta-Tutoring Project: Lessons Learned

      • Kurt VanLehn, Winslow Burleson, Sylvie Girard, Maria Elena Chavez-Echeagaray, Javier Gonzalez-Sanchez, Yoalli Hidalgo-Pontet et al.

      Pages 84-93

   6. Identifying Learning Conditions that Minimize Mind Wandering by Modeling Individual Attributes

      • Kristopher Kopp, Robert Bixler, Sidney D’Mello

      Pages 94-103

   7. Investigating the Effect of Meta-cognitive Scaffolding for Learning by Teaching

      • Noboru Matsuda, Cassondra L. Griger, Nikolaos Barbalios, Gabriel J. Stylianides, William W. Cohen, Kenneth R. Koedinger

      Pages 104-113

4. Collaborative Learning

   1. Togetherness: Multiple Pedagogical Conversational Agents as Companions in Collaborative Learning

      • Yugo Hayashi

      Pages 114-123

   2. What Works: Creating Adaptive and Intelligent Systems for Collaborative Learning Support

      • Nia M. Dowell, Whitney L. Cade, Yla Tausczik, James Pennebaker, Arthur C. Graesser

      Pages 124-133

   3. Using an Intelligent Tutoring System to Support Collaborative as well as Individual Learning

      • Jennifer K. Olsen, Daniel M. Belenky, Vincent Aleven, Nikol Rummel

      Pages 134-143

5. Data Mining and Student Behavior

   1. Bayesian Student Modeling Improved by Diagnostic Items

      • Yang Chen, Pierre-Henri Wuillemin, Jean-Marc Labat

      Pages 144-149

   2. Learning Bayesian Knowledge Tracing Parameters with a Knowledge Heuristic and Empirical Probabilities

      • William J. Hawkins, Neil T. Heffernan, Ryan S. J. D. Baker

      Pages 150-155

   3. Investigate Performance of Expected Maximization on the Knowledge Tracing Model

      • Junjie Gu, Hang Cai, Joseph E. Beck

      Pages 156-161

This book constitutes the refereed proceedings of the 12th International Conference on Intelligent Tutoring Systems, ITS 2014, held in Honolulu, HI, USA, in June 2014. The 31 revised full papers, 45 short papers and 27 posters presented were carefully viewed and selected from 177 submissions. The specific theme of the ITS 2014 conference is "Creating fertile soil for learning interactions". Besides that, the highly interdisciplinary ITS conferences bring together researchers in computer science, learning sciences, cognitive and educational psychology, sociology, cognitive science, artificial intelligence, machine learning and linguistics. The papers are organized in topical sections on affect; multimodality and metacognition; collaborative learning; data mining and student behavior; dialogue and discourse; generating hints, scaffolds and questions; game-based learning and simulation; graphical representations and learning; student strategies and problem solving; scaling ITS and assessment.

• bayesian networks
• clustering
• collaborative learning
• computational thinking
• data mining
• machine learning algorithms
• natural language processing
• recommender system
• reinforcement learning
• semi-supervised learning
• symbolic and algebraic manipulation
• visualizations

• Computer Science Department, University Politehnica of Bucharest, Bucharest, Romania

  Stefan Trausan-Matu

• North Carolina State University, Raleigh, USA

  Kristy Elizabeth Boyer

• Department of Information and Computer Sciences, University of Hawaii, Honolulu, USA

  Martha Crosby

• Neoanalysis Ltd., Athens, Greece

  Kitty Panourgia

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