International Conference on Educational Data Mining (EDM 2020 )

June 10- 13 ,2020,Ifrane, Morocco

Scope & Topics

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, and multimodal streams such as eye-tracking and other sensor data. 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.

Topics of interest include, but are not limited to, the following

• 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 interactions 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 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.

Paper Submission

All accepted papers will be published in the open-access proceedings of the conference, with the exception of the Journal track as stated above. All paper submissions will be evaluated using double-blind reviewing. Prospective authors should blind their papers, hiding author names and affiliations in their original submission, so that they can be evaluated on their merits.

Important Dates