Check the link from the menu for the whole list.
Come and help to the conference success, and earn free registration.
Organized by the International Working Group on Educational Data Mining.



EDM 2009 Accepted Papers:

  • Elizabeth Ayers, Rebecca Nugent and Nema Dean. A Comparison of Student Skill Knowledge Estimates
  • Ryan Baker. Differences Between Intelligent Tutor Lessons, and the Choice to Go Off-Task
  • Dror Ben-Naim, Michael Bain and Nadine Marcus. A User-Driven and Data-Driven Approach for Supporting Teachers in Reflection and Adaptation of Adaptive Tutorials
  • Javier Bravo Agapito, Sergey Sosnovsky and Alvaro Ortigosa. Detecting Symptoms of Low Performance Using Production Rules
  • Gerben Dekker, Mykola Pechenizkiy and Jan Vleeshouwers. Predicting Students Drop Out: A Case Study
  • Mingyu Feng, Joseph Beck and Neil Heffernan. Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions on Learning
  • Yue Gong, Dovan Rai, Joseph Beck and Neil Heffernan. Does Self-Discipline impact students’ knowledge and learning?
  • Arnon Hershkovitz and Rafi Nachmias. Consistency of Students' Pace in Online Learning
  • Tara Madhyastha and Steven Tanimoto. Student Consistency and Implications for Feedback in Online Assessment Systems
  • Ryo Nagata, Keigo Takeda, Koji Suda, Junichi Kakegawa and Koichiro Morihiro. Edu-mining for Book Recommendation for Pupils
  • Rebecca Nugent, Elizabeth Ayers and Nema Dean. Conditional Subspace Clustering of Skill Mastery: Identifying Skills that Separate Students
  • Zachary Pardos and Neil Heffernan. Determining the Significance of Item Order In Randomized Problem Sets
  • Philip I Pavlik Jr., Hao Cen and Kenneth R. Koedinger. Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models
  • David Prata, Ryan Baker, Evandro Costa, Carolyn Rose and Yue Cui. Detecting and Understanding the Impact of Cognitive and Interpersonal Conflict in Computer Supported Collaborative Learning Environments
  • Dovan Rai, Yue Gong and Joseph Beck. Using Dirichlet Priors to Improve Model Parameter Plausibility
  • Steven Ritter, Thomas Harris, Tristan Nixon, Daniel Dickison, R. Charles Murray and Brendon Towle. Reducing the Knowledge Tracing Space
  • Vasile Rus, Mihai Lintean and Roger Azevedo. Automatic Detection of Student Mental Models During Prior Knowledge Activation in MetaTutor
  • Marián Šimko and Maria Bielikova. Automatic Concept Relationships Discovery for an Adaptive E-course
  • John Stamper and Tiffany Barnes. An Unsupervised, Frequency-based Metric for Selecting Hints in an MDP-based Tutor
  • Cesar Vialardi Sacin, Javier Bravo Agapito, Leila Shafti and Alvaro Ortigosa. Recommendation in Higher Education Using Data Mining Techniques

EDM 2009 Accepted Posters

  • Safia Abbas and Hajime Sawamura. Developing an Argument Learning Environment Using Agent-Based ITS (ALES)
  • Antonio R. Anaya and Jesus G. Boticario. A Data Mining Approach to Reveal Representative Collaboration Indicators in Open Collaboration Frameworks
  • Dave Barker-Plummer, Richard Cox and Robert Dale. Dimensions of Difficulty in Translating Natural Language into First-Order Logic
  • Suleyman Cetintas, Luo Si, Yan Ping Xin and Casey Hord. Predicting Correctness of Problem Solving from Low-level Log Data in Intelligent Tutoring Systems
  • Ming Feng and Joseph Beck. Back to the Future: A Non-automated Method of Constructing Transfer Models
  • Sharon Hardof-Jaffe, Arnon Hershkovitz, Hama Abu-Kishk, Ofer Bergman and Rafi Nachmias. How do Students Organize Personal Information Spaces?
  • Cecily Heiner and Joseph Zachary. Improving Student Question Classification
  • Tarsis Marinho, Lucas M. Braz, Diego Dermeval, Rafael Ferreira Leite de Melo, Elvys Soares, Ig Ibert Bittencourt and Evandro Barros Costa. SEDAM: Semantic Educational Data Mining
  • Jack Mostow and Joseph Beck. What, How, and Why should Tutors Log?
  • Mykola Pechenizkiy, Nikola Trcka, Ekaterina Vasilyeva, Wil van der Aalst and Paul De Bra. Process Mining Online Assessment Data
  • José Ramón Quevedo and Elena Montañés. Obtaining Weights of a Rubric Through a Pairwise Learning Model When the Assessment Process Involves More than One Lecturer
  • Cristobal Romero, Sebastián Ventura, Enrique García and Carlos de Castro. Collaborative Data Mining Tool for Education
  • Amelia Zafra and Sebastián Ventura. Predicting Student Grades in Learning Management Systems with Multiple Instance Genetic Programming
  • Lukáš Zoubek and Michal Burda. Visualization of Differences in Data Measuring Mathematical Skills