Improving Learning Outcomes for All Learners

Educational Data Mining is a leading international forum for high-quality research that mines datasets to answer educational research questions, including exploring how people learn and how they teach. These data may originate from a variety of learning contexts, including learning and information 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, and additional databases of student information. The overarching goal of the Educational Data Mining research community is to support learners and teachers more effectively, by developing data-driven understandings of the learning and teaching processes in a wide variety of contexts and for diverse learners.

The theme of this year’s conference is “Improving Learning Outcomes for All Learners”. The theme comprises two parts: (1) Identifying actionable learning or teaching strategies that can be used to improve learning outcomes. (2) Using EDM to promoting more equitable learning across diverse groups of learners. For this 13th iteration of the conference we specifically welcome research that advances aforementioned areas.

Topics of interest

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

  • Causal inference of which factors impact -not just predict- students’ learning.
  • Developing and applying fairer learning algorithms that exhibit similar performance across subgroups of students, and detecting instances of algorithmic unfairness in existing methods.
  • Replicating previous studies with larger sample sizes, in different domains, and/or in more diverse contexts.
  • Modeling student and group interaction for collaborative and/or competitive problem-solving.
  • EDM for gamification and in educational games.
  • Deriving representations of domain knowledge from data.
  • Modeling real-world problem solving in open-ended domains.
  • Modeling and detecting students’ affective states and cognitive states (e.g., engagement, confusion) with multimodal data.
  • Ethical considerations in EDM.
  • Closing the loop between EDM research and educational outcomes to yield actionable advice.
  • Informing data mining research with educational and/or motivational theory.
  • Developing new techniques for mining educational data.
  • Data mining to understand how learners interact in formal and informal educational contexts.
  • Bridging the gap between data mining and learning sciences.
  • Legal and social policies to govern EDM.
  • Automatically assessing student knowledge.
  • Social network analysis of student and teacher interactions.