Seventeenth International Conference on Educational Data Mining (EDM 2024)

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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 17th iteration of the conference, EDM 2024, will take place in a hybrid format, both online and in-person, to facilitate participation and networking for all.

The theme of this year’s conference is “New tools, new prospects, new risks – educational data mining in the age of generative AI”. This year’s theme focuses on the movement from descriptive and predictive models to generative artificial intelligence (AI) and what that means for learning environments and processes. While the new methods unlock exciting new potentials for educational data mining, they also foreground many ethical considerations and risks that are associated with all types of machine learning and artificial intelligence. In addition to the general topics listed below, we welcome research in the following areas: mitigating biases and harms that may result from model use, accounting for the stereotypes that are inherent to the large models that drive generative AI, separating the hype surrounding these new technologies from their potential in educational settings, and finding ways to use these models to better understand learning processes and support learning.



15 Jul. 2024
19 Jul. 2024





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