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 log files, student-produced artifacts, discourse, learning content and context, sensor data, and multi-resource and multimodal streams. 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 14th iteration of the conference, EDM 2021, will take place in an online format.
The theme of this year’s conference is “Improving Blended and Distance Learning” (BDL). The theme focuses on identifying learning or teaching strategies that can be used to improve learning in various formats, such as partially or fully online, synchronous or asynchronous, and centralized or federated. In addition to the general topics listed below, we welcome research in the following areas: receiving implicit and explicit feedback from learners in BDL environments, interacting with students to ensure no learner is left behind, integrating and utilizing learning analytics in BDL environments to cope with switching between in-person and online modes, and addressing emerging privacy and ethical challenges in the new learning setting.