Educational Data Mining in Open-Ended Domains

General Call for Papers (open for posters until March 24)
Call for Industry Track Papers (PDF) (closed)
Workshops and Tutorials (consult individual workshop page for submission details)

Educational Data Mining is a leading international forum for high-quality research that mines data sets to answer educational research questions that shed light on the learning process. These data sets may originate from a variety of learning contexts, including learning management systems, interactive learning environments, intelligent tutoring systems, educational games, and data-rich learning activities. Educational data mining considers a wide variety of types of data, including but not limited to raw log files, student-produced artifacts, discourse, multimodal streams such as eye-tracking, and other sensor data. The overarching goal of the Educational Data Mining research community is to better support learners by developing data-driven understandings of the learning process in a wide variety of contexts and for diverse learners.

The theme of this year’s conference is EDM in Open-Ended Domains.  As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks such as writing, design, and collaborative problem-solving.  And it has been used in new informal contexts where student actions are at best semi-structured. For this 12th iteration of the conference, we specifically welcome research in these new areas.  

Topics of interest

Topics of interest to the conference include but are not limited to.

  • Modeling student and group interaction for guidance and collaborative problem-solving.
  • Deriving representations of domain knowledge from data.
  • Modeling real-world problem solving in open-ended domains.
  • Detecting and addressing students’ affective and emotional states.
  • Informing data mining research with educational theory.
  • Developing new techniques for mining educational data.
  • Data mining to understand how learners interact in formal and informal educational contexts.
  • Modeling students’ affective states and engagement with multimodal data.
  • Synthesizing rich data to inform students and educators.
  • Bridging data mining and learning sciences.
  • Applying social network analysis to support student interactions.
  • Legal and social policies to govern EDM.
  • Developing generic frameworks, techniques, research methods, and approaches for EDM.
  • Closing the loop between EDM research and educational outcomes to yield actionable advice.
  • Automatically assessing student knowledge.