Latest news:


May 11, 2012
Category: General
Posted by: admin

The early registration period has ended. Regular registration is now open.


Apr 19, 2012
Category: General
Posted by: admin

Early registration fees are $350 for faculty and professionals.  The registration fee is $200 for graduate students.  For this fee, you must present a valid student ID when you pick up your conference packet.

Apr 10, 2012
Category: General
Posted by: admin

Please see the proceedings for a complete list of accepted papers.


Feb 28, 2012
Category: General
Posted by: admin
See the Program page for more information!

Organized by the International Educational Data Mining Society (IEDMS).


Gold Sponsors

Carnegie Learning

Pearson Education

Silver Sponsors


Conference Schedule

Click here to download the conference schedule including session descriptions.

Tentative Conference Schedule

Full papers have 30 minutes each (including questions); Short papers have 15 minutes each (including questions)

The poster sizes should be A0 size or smaller, and portrait orientation. A0 is approximately 84cm x 119cm, or 33" x 47"

DAY 1: Tuesday, 19 June 2012

8:00-8:40 Registration
9:00–10:00 Keynote 1
Danielle S. McNamara
10:00–10:30 MORNING BREAK
10:30–12:30 Session I: Understanding Student Learning
3 Full + 2 Short [list of papers]
12:30–14:00 LUNCH
14:00–16:00 Session II: Knowledge Tracing
3 Full + 2 Short [list of papers]
16:30-17:30 Session III: Student Modeling
1 Full + 2 Short [list of papers]
Evening Opening Reception

DAY 2: Wednesday, 20 June 2012

8:30-9:00 Registration
9:00–10:00 Keynote 2
Myra Spiliopoulou
10:00–10:30 MORNING BREAK
10:30–12:30 Session IV: Predictive Modeling
3 Full + 2 Short [list of papers]
12:30–14:00 LUNCH
14:00–16:00 Session V: Intelligent Tutoring
3 Full + 2 Short [list of papers]
16:30–16:50 Poster Session: 1 Minute Madness
Each poster presenter gets 1 minute and 1 slide
16:50-18:00 Poster and Demonstration Session
17 Posters [list of posters and demonstrations]
Evening Conference Banquet

DAY 3: Thursday, 21 June 2012

8:30-9:00 Registration
9:00–10:00 Keynote 3
John Behrens and Bob Dolan
10:00-10:30 MORNING BREAK
10:30-12:30 Session VI: Learning Behavior / Affect
3 Full + 2 Short [list of papers]
12:30-14:00 LUNCH
14:00-15:15 Session VII: Methods and Tools for EDM
1 Full + 3 Short [list of papers]
15:15–16:00 EDM Community Meeting

List of Papers and Posters by Session

Session I: Understanding student learning [?]
Tuesday, June 19, 10:30–12:30
Chair: Tiffany Barnes

  • Searching for Variables and Models to Investigate Mediators of Learning from Multiple Representations
    Martina Rau and Richard Scheines[pdf]
  • Identifying Students' Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning
    François Bouchet, John Kinnebrew, Gautam Biswas and Roger Azevedo[pdf]
  • Assisting Instructor Assessment of Undergraduate Collaborative Wiki and SVN Activities
    Jihie Kim, Erin Shaw, Hao Xu and Adarsh G V[pdf]
  • Mining Concept Maps to Understand Students' Learning
    Jin Soung Yoo and Moon-Heum Cho[pdf]
  • Learning Gains for Core Concepts in a Serious Game on Scientific Reasoning
    Carol Forsyth, Philip Pavlik Jr, Arthur Graesser, Zhiqiang Cai, Mae-Lynn Germany, Keith Millis, Heather Butler, Diane Halpern and Robert Dolan[pdf]

Session II: Knowledge Tracing [?]
Tuesday, June 19, 14:00-16:00
Chair: Ryan Baker

  • Dynamic Cognitive Tracing: Towards Unified Discovery of Student and Cognitive Models
    José González-Brenes and Jack Mostow[pdf]
  • The Impact on Individualizing Student Models on Necessary Practice Opportunities
    Jung In Lee and Emma Brunskill[pdf]
  • Comparison of methods to trace multiple subskills: Is LR-DBN best?
    Yanbo Xu and Jack Mostow[pdf]
  • Investigating Practice Schedules of Multiple Fraction Representations Using Knowledge Tracing Based Learning Analysis Techniques
    Martina Rau and Zachary Pardos[pdf]
  • Leveraging First Response Time into the Knowledge Tracing Model
    Yutao Wang and Neil Heffernan[pdf]

Session III: Student Modeling [?]
Tuesday, June 19, 16:30-17:30
Chair: Mykola Pechenizkiy

  • Automated Student Model Improvement
    Kenneth R. Koedinger, Elizabeth A. McLaughlin and John C. Stamper[pdf]
  • Using Student Modeling to Estimate Student Knowledge Retention
    Yutao Wang and Joseph Beck[pdf]
  • Policy Building -- An Extension To User Modeling
    Michael Yudelson and Emma Brunskill[pdf]

Session IV: Predictive Modeling [?]
Wednesday, June 20, 10:30-12:35
Chair: Michel Desmarais

  • Using Edit Distance to Analyse Errors in a Natural Language to Logic Translation Corpus
    Dave Barker-Plummer, Robert Dale, and Richard Cox[pdf]
  • Predicting drop-out from social behaviour of students
    Tomas Obsivac, Lubos Popelinsky, Jaroslav Bayer, Jan Geryk and Hana Bydzovska[pdf]
  • Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction
    Shubhendu Trivedi, Zachary Pardos, Gábor Sárközy and Neil Heffernan[pdf]
  • Classification via clustering for predicting final marks starting from the student participation in Forums
    Manuel Ignacio Lopez, Cristobal Romero and Sebastián Ventura[pdf]
  • The real world significance of performance prediction
    Zachary Pardos, Qing Yang Wang and Shubhendu Trivedi[pdf]

Session V: Intelligent Tutoring [?]
Wednesday, June 20, 14:00-16:00
Chair: Neil Heffernan

  • Learner Differences in Hint Processing
    Ilya Goldin, Kenneth Koedinger and Vincent Aleven[pdf]
  • Automatic Discovery of Speech Act Categories in Educational Games
    Vasile Rus, Arthur Graesser, Cristian Moldovan and Nobal Niraula[pdf]
  • Methods to find the number of latent skills
    Behzad Beheshti, Michel Desmarais and Rhouma Naceur[pdf]
  • Interaction Networks: Generating High Level Hints Based on Network Community Clusterings
    Michael Eagle, Matthew Johnson and Tiffany Barnes[pdf]
  • Calculating Probabilistic Distance to Solution in a Complex Problem Solving Domain
    Leigh Ann Sudol, Kelly Rivers and Thomas K. Harris[pdf]

Session VI: Learning Behavior / Affect [?]
Thursday, June 21, 10:30-12:30
Chair: Jack Mostow

  • Mining Student Behavior Patterns in Reading Comprehension Tasks
    Terry Peckham and Gord McCalla[pdf]
  • Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution
    John Kinnebrew and Gautam Biswas[pdf]
  • Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra
    Ryan S.J.D. Baker, Sujith Gowda, Michael Wixon, Jessica Kalka, Angela Wagner, Aatish Salvi, Vincent Aleven, Gail Kusbit, Jaclyn Ocumpaugh and Lisa Rossi[pdf]
  • Early Prediction of Student Self-Regulation Strategies by Combining Multiple Models
    Jennifer Sabourin, Bradford Mott and James Lester[pdf]
  • Identifying Successful Learners from Interaction Behaviour
    Judi McCuaig and Julia Baldwin[pdf]

Session VII: Methods and Tools for EDM [?]
Thursday, June 21, 14:00-15:15
Chair: Agathe Merceron

  • Model-Based Collaborative Filtering Analysis of Student Response Data: Machine-Learning Item Response Theory
    Yoav Bergner, Stefan Droschler, Gerd Kortemeyer, Saif Rayyan, Daniel Seaton and David Pritchard[pdf]
  • The Rise of the Super Experiment
    John Stamper, Derek Lomas, Dixie Ching, Steven Ritter, Kenneth Koedinger and Jonathan Steinhart[pdf]
  • Development of a Workbench to Address the Educational Data Mining Bottleneck
    Ma. Mercedes Rodrigo, Ryan S. J. D. Baker, Bruce McLaren, Alejandra Jayme and Thomas Dy[pdf]
  • Meta-learning Approach for Automatic Parameter Tuning: A case of study with educational datasets
    María De Mar Molina, Cristobal Romero and Sebastián Ventura[pdf]

Poster and Demo Session [?]
Wednesday, June 20, 16:00-18:00


  • Social Networks Analysis for Quantifying Students’ Performance in Teamwork
    Pedro Crespo and Claudia Antunes[pdf]
  • Data mining techniques for design of ITS student models
    Ritu Chaturvedi and Christie Ezeife[pdf]
  • Variable Construction and Causal Discovery for Cognitive Tutor Log Data: Initial Results
    Stephen Fancsali[pdf]
  • A promising classification method for predicting distance students’ performance.
    Diego García-Saiz and Marta Zorrilla[pdf]
  • Deciding on Feedback Polarity and Timing
    Stuart Johnson and Osmar Zaiane[pdf]
  • Automated Detection of Mentors and Players in an Educational Game
    Fazel Keshtkar, Brent Morgan and Arthur Graesser[pdf]
  • Stress Analytics in Education
    Rafal Kocielnik, Mykola Pechenizkiy and Natalia Sidorova[pdf]
  • Speaking (and touching) to learn: a method for mining the digital footprints of face-to-face collaboration
    Roberto Martinez Maldonado, Kalina Yacef and Judy Kay[pdf]
  • Learning Paths in a Non-Personalizing e-Learning Environment
    Agathe Merceron, Liane Beuster, Margarita Elkina, Albrecht Fortenbacher, Leonard Kappe, Andreas Pursian, Sebastian Schwarzrock and Boris Wenzlaff[pdf]
  • Analyzing paths in a student database
    Donatella Merlini, Renza Campagni and Renzo Sprugnoli[pdf]
  • CurriM: Curriculum Mining
    Mykola Pechenizkiy, Nikola Trcka, Paul De Bra and Pedro Toledo[pdf]
  • Inferring learners’ knowledge from observed actions
    Anna Rafferty, Michelle Lamar and Thomas Griffiths[pdf]
  • Analyzing the behavior of a teacher network in a Web 2.0 environment
    Eliana Scheihing, Carolina Aros and Daniel Guerra[pdf]
  • Finding Dependent Test Items: An Information Theory Based Approach
    Xiaoxun Sun[pdf]
  • Similarity Functions for Collaborative Master Recommendations
    Alexandru Surpatean, Evgueni Smirnov and Nicolai Manie[pdf]
  • Fit-to-Model Statistics for Evaluating Quality of Bayesian Student Ability Estimation
    Ling Tan[pdf]
  • Categorizing Students' Response Patterns using the Concept of Fractal Dimension
    Rasil Warnakulasooriya and William Galen[pdf]