Educational Data Mining in Open-Ended Domains


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.

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