Personalization in EDM and LAK - Dataset

This dataset compiles a structured review of research studies on personalization in educational systems. It aggregates publications across conferences and years, focusing on whether and how personalization is implemented and defined within learning environments. The dataset captures multiple dimensions of personalization, including learning path adaptation, recommender systems, dashboards, feedback mechanisms, instructional strategies, scheduling, and collaborative tools.

In addition, it records contextual and methodological attributes such as educational setting, participant scale, technical approaches, evaluation metrics, and reported limitations. To enable deeper analysis, the dataset further encodes outcomes and signals related to learner behavior, knowledge mastery, engagement, content interaction, and system-level diagnostics, alongside administrative and artifact-based data.

Overall, this dataset provides a comprehensive foundation for analyzing trends, methods, and impacts of personalization in education, supporting comparative studies and meta-analyses across diverse learning scenarios and technological approaches.