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05/12/09
Accepted Papers and Posters
Check the link from the menu for the whole list.
03/16/09
Call for Student Volunteers
Come and help to the conference success, and earn free registration.
Organized by the International Working Group on Educational Data Mining.

 

Sponsors:





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