Intelligent Tutoring Systems

1. Data Mining and Student Behavior

   1. Understanding Wheel Spinning in the Context of Affective Factors

      • Joseph Beck, Ma. Mercedes T. Rodrigo

      Pages 162-167

   2. The Usefulness of Log Based Clustering in a Complex Simulation Environment

      • Samad Kardan, Ido Roll, Cristina Conati

      Pages 168-177

   3. Survival Analysis on Duration Data in Intelligent Tutors

      • Michael Eagle, Tiffany Barnes

      Pages 178-187

   4. Beyond Knowledge Tracing: Modeling Skill Topologies with Bayesian Networks

      • Tanja Käser, Severin Klingler, Alexander Gerhard Schwing, Markus Gross

      Pages 188-198

2. Dialogue and Discourse

   1. Identifying Effective Moves in Tutoring: On the Refinement of Dialogue Act Annotation Schemes

      • Alexandria Katarina Vail, Kristy Elizabeth Boyer

      Pages 199-209

   2. When Is Tutorial Dialogue More Effective Than Step-Based Tutoring?

      • Min Chi, Pamela Jordan, Kurt VanLehn

      Pages 210-219

   3. Predicting Student Learning from Conversational Cues

      • David Adamson, Akash Bharadwaj, Ashudeep Singh, Colin Ashe, David Yaron, Carolyn P. Rosé

      Pages 220-229

   4. Validating the Automated Assessment of Participation and of Collaboration in Chat Conversations

      • Mihai Dascalu, Ştefan Trausan-Matu, Philippe Dessus

      Pages 230-235

   5. Context-Based Speech Act Classification in Intelligent Tutoring Systems

      • Borhan Samei, Haiying Li, Fazel Keshtkar, Vasile Rus, Arthur C. Graesser

      Pages 236-241

   6. Macro-adaptation in Conversational Intelligent Tutoring Matters

      • Vasile Rus, Dan Stefanescu, William Baggett, Nobal Niraula, Don Franceschetti, Arthur C. Graesser

      Pages 242-247

   7. An Evaluation of Self-explanation in a Programming Tutor

      • Amruth N. Kumar

      Pages 248-253

   8. Identifying Thesis and Conclusion Statements in Student Essays to Scaffold Peer Review

      • Mohammad Hassan Falakmasir, Kevin D. Ashley, Christian D. Schunn, Diane J. Litman

      Pages 254-259

   9. Can Diagrams Predict Essay Grades?

      • Collin F. Lynch, Kevin D. Ashley, Min Chi

      Pages 260-265

   10. Toward Automatic Inference of Causal Structure in Student Essays

       • Peter Hastings, Simon Hughes, Anne Britt, Dylan Blaum, Patty Wallace

       Pages 266-271

   11. Classroom Evaluation of a Scaffolding Intervention for Improving Peer Review Localization

       • Huy Nguyen, Wenting Xiong, Diane Litman

       Pages 272-282

   12. Comprehension SEEDING: Comprehension through Self Explanation, Enhanced Discussion, and INquiry Generation

       • Frank Paiva, James Glenn, Karen Mazidi, Robert Talbot, Ruth Wylie, Michelene T. H. Chi et al.

       Pages 283-293

3. Generating Hints, Scaffolds, and Questions

   1. Pedagogical Evaluation of Automatically Generated Questions

      • Karen Mazidi, Rodney D. Nielsen

      Pages 294-299

   2. Content-Dependent Question Generation for History Learning in Semantic Open Learning Space

      • Corentin Jouault, Kazuhisa Seta

      Pages 300-305

   3. Data-Driven Program Synthesis for Hint Generation in Programming Tutors

      • Timotej Lazar, Ivan Bratko

      Pages 306-311

   4. Building Games to Learn from Their Players: Generating Hints in a Serious Game

      • Andrew Hicks, Barry Peddycord III, Tiffany Barnes

      Pages 312-317

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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