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About the Conference

EDM 2018 Program

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

Call for paper