View source for EDM 2020

== Topics ==

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.