Abstract is missing.
- Data Mining Algorithms to Classify StudentsCristóbal Romero, Sebastián Ventura, Pedro G. Espejo, César Hervás. 8-17 [doi]
- Acquiring Background Knowledge for Intelligent Tutoring SystemsCláudia Antunes. 18-27 [doi]
- Analytic Comparison of Three Methods to Evaluate Tutorial BehaviorsJack Mostow, Xiaonan Zhang. 28-37 [doi]
- Labeling Student Behavior Faster and More Precisely with Text ReplaysRyan Baker, Adriana de Carvalho. 38-47 [doi]
- Adaptive Test Design with a Naive Bayes FrameworkMichel C. Desmarais, Alejandro Villarreal, Michel Gagnon. 48-56 [doi]
- Interestingness Measures for Associations Rules in Educational DataAgathe Merceron, Kalina Yacef. 57-66 [doi]
- Improving Contextual Models of Guessing and Slipping with a Trucated Training SetRyan Shaun Joazeiro de Baker, Albert T. Corbett, Vincent Aleven. 67-76 [doi]
- Using Item-type Performance Covariance to Improve the Skill Model of an Existing TutorPhilip Pavlik, Hao Cen, Lili Wu, Kenneth R. Koedinger. 77-86 [doi]
- Data-driven modelling of students interactions in an ILEManolis Mavrikis. 87-96 [doi]
- Integrating Knowledge Gained From Data Mining With Pedagogical KnowledgeRoland Hübscher, Sadhana Puntambekar. 97-106 [doi]
- Can an Intelligent Tutoring System Predict Math Proficiency as Well as a Standarized Test?Mingyu Feng, Joseph E. Beck, Neil T. Heffernan, Kenneth R. Koedinger. 107-116 [doi]
- A Response Time Model For Bottom-Out Hints as Worked ExamplesBenjamin Shih, Kenneth R. Koedinger, Richard Scheines. 117-126 [doi]
- Mining Student Behavior Models in Learning-by-Teaching EnvironmentsHogyeong Jeong, Gautam Biswas. 127-136 [doi]
- Argument graph classification with Genetic Programming and C4.5Collin Lynch, Kevin D. Ashley, Niels Pinkwart, Vincent Aleven. 137-146 [doi]
- The Composition Effect: Conjuntive or Compensatory? An Analysis of Multi-Skill Math Questions in ITSZachary A. Pardos, Neil T. Heffernan, Carolina Ruiz, Joseph E. Beck. 147-156 [doi]
- An Open Repository and analysis tools for fine-grained, longitudinal learner dataKenneth R. Koedinger, Kyle Cunningham, Alida Skogsholm, Brett Leber. 157-166 [doi]
- Mining Data from an Automated Grading and Testing System by Adding Rich Reporting CapabilitiesAnthony Allevato, Matthew Thornton, Stephen H. Edwards, Manuel A. Pérez-Quiñones. 167-176 [doi]
- Analyzing Rule Evaluation Measures with Educational Datasets: A Framework to Help the TeacherSebastián Ventura, Cristóbal Romero, César Hervás. 177-181 [doi]
- Mining and Visualizing Visited Trails in Web-Based Educational SystemsCristóbal Romero, Sergio Gutiérrez, Manuel Freire, Sebastián Ventura. 182-186 [doi]
- Mining the Student Assessment Data: Lessons Drawn from a Small Scale Case StudyMykola Pechenizkiy, Toon Calders, Ekaterina Vasilyeva, Paul De Bra. 187-191 [doi]
- Machine Classification of Peer Comments in PhysicsKwangsu Cho. 192-196 [doi]
- A pilot study on logic proof tutoring using hints generated from historical student dataTiffany Barnes, John C. Stamper, Lorrie Lehman, Marvin J. Croy. 197-201 [doi]
- Towards Argument Mining from Relational DataBaseSafia Abbas, Hajime Sawamura. 202-209 [doi]
- Skill Set Profile Clustering Based on Weighted Student ResponsesElizabeth Ayers, Rebecca Nugent, Nema Dean. 210-217 [doi]
- Can we predict which groups of questions students will learn from?Mingyu Feng, Neil T. Heffernan, Joseph E. Beck, Kenneth R. Koedinger. 218-225 [doi]
- Developing a Log-based Motivation Measuring ToolArnon Hershkovitz, Rafi Nachmias. 226-233 [doi]
- Mining Free-form Spoken Responses to Tutor PromptsXiaonang Zhang, Jack Mostow, Nell Duke, Christina Trotochaud, Joseph Valeri, Albert T. Corbett. 234-241 [doi]
- Computational Infrastructures for School Improvement: A Way to Move ForwardR. Benjamin Shapiro, Hisham Petry, Louis M. Gomez. 242-249 [doi]
- A Preliminary Analysis of the Logged Questions that Students Ask in Introductory Computer ScienceCecily Heiner. 250-257 [doi]
- Reinforcement Learning-based Feature Seleciton For Developing Pedagogically Effective Tutorial Dialogue TacticsMin Chi, Pamela W. Jordan, Kurt VanLehn, Moses Hall. 258-265 [doi]
- Do Students Who See More Concepts in an ITS Learn More?Moffat Mathews, Tanja Mitrovic. 266-273 [doi]