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Knowledge Graph Implementation and Contextual, Graph-based Learning Path Recommendation Roadmap for the Personalized Learning Environment in the OSCAR Project

Authors: Christian Weber; Hasan Abu-Rasheed;

Knowledge Graph Implementation and Contextual, Graph-based Learning Path Recommendation Roadmap for the Personalized Learning Environment in the OSCAR Project

Abstract

In the scope of OSCAR project's personalized learning environment (eDoer), student decisions about their learning paths are supported with a recommender system. The recommendation provides the user with suggestions about topics and learning materials to study, in order to gain skills that enable them to perform a corresponding job. The knowledge graph based recommender system is designed to utilize the collected data about users, jobs, skills, topics, and OERs, in which the eDoer platform is organized into, in order to predict the relations between a certain user and the skills, topics or OERs that enables them to reach their pre-defined learning goals best. The focus on the graph not only enables contextualized decisions, but also provides the structural richness to provide multi-element recommendations as learning paths. More information on the project: http://oscar-ai.eu/ Learning and mentoring platform: http://edoer.eu/

The OSCAR project and this deliverable was supported by the European Commission Erasmus Plus programme. Project id: 2020-1-DE01-KA203-005713

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Keywords

online education, knowledge graph, edoer, personalized learning, artificial intelligence

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