About the Conference
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.
Topics of interest
Topics of interest to the conference include, but are not limited to.
- Deriving representations of domain knowledge from data.
- Detecting and addressing students’ affective and emotional states.
- Informing data mining research with educational theory.
- Contributing to theories of learning through data mining.
- Data mining to understand how learners interact with emerging genres of pedagogical environments such as educational games, MOOCs, and exploratory learning environments.
- Analyzing multimodal and sensor data.
- Using data mining methods to provide support for teachers, parents and policy makers.
- Bridging data mining and learning sciences.
- Adapting state-of-the-art data mining approaches to the educational domain.
- Building an understanding of social and collaborative learning processes through data mining.
- Developing generic frameworks, techniques, research methods, and approaches for educational data mining.
- Closing the loop between education data research and educational outcomes.
- Automatically assessing student knowledge.
- Evaluating the efficacy of curriculum and interventions