The growth of data in the knowledge society creates opportunities for new insights through advanced analysis methods based on information retrieval technologies. Educational institutions also create and own huge datasets on their students and course activities. But they make little use of the data when considering new educational services, recommending suitable peers or content, and improving the personalization of learning. Nevertheless, personalized learning is expected to have the potential to create more effective learning experiences, and accelerate the study time for students. In the educational world, only very limited datasets are publicly available and no agreed quality standards exist on the personalization of learning.
The dataTEL Theme Team aims to address these issues by advancing data driven research to gain verifiable and valid results and to develop a body of knowledge about the personalization of learning. In this context, new challenges emerge like unclear ethical, legal and privacy issues, suitable policies and formats to share data, required pre-processing procedures and rules to create sharable datasets, common evaluation criteria for personalization and recommender systems in TEL.
The lecture will give an overview about the latest developments in educational datasets research and give an outline how a dataset driven future in TEL could look like.

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