With the increased dependence on online learning platforms and educational resource repositories, a unified representation of digital learning resources becomes essential to support a dynamic and multi-source learning experience. We introduce the EduCOR ontology, an educational, career-oriented ontology that provides a foundation for representing online learning resources for personalised learning systems. The ontology is designed to enable learning material repositories to offer learning path recommendations, which correspond to the user’s learning goals and preferences, academic and psychological parameters, and labour-market skills. We present the multiple patterns that compose the EduCOR ontology, highlighting its cross-domain applicability and integrability with other ontologies. A demonstration of the proposed ontology on the real-life learning platform eDoer is discussed as a use case. We evaluate the EduCOR ontology using both gold standard and task-based approaches. The comparison of EduCOR to three gold schemata, and its application in two use-cases, shows its coverage and adaptability to multiple OER repositories, which allows generating user-centric and labour-market oriented recommendations.Resource: https://tibonto.github.io/educor/.

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In our position paper on a technology-enhanced smart learning environment, we propose the innovative combination of a knowledge graph representing what one has to learn and a learning path defining in which order things are going to be learned. In this way, we aim to identify students' weak spots or knowledge gaps in order to individually assist them in reaching their goals. Based on the performance of different learning paths, one might further identify the characteristics of a learning system that leads to successful students. In addition, by studying assessments and the different ways a particular problem can be solved, new methods for a multi-dimensional classification of assessments can be developed. The theoretical findings on learning paths in combination with the classification of assessments will inform the design and development of a smart learning environment. By combining a knowledge graph with different learning paths and the corresponding practical assessments we enable the creation of a smart learning tool. While the proposed approach can be applied to different educational domains and should lead to more effective learning environments fostering deep learning in schools as well as in professional settings, in this paper we focus on the domain of mathematics in primary and high schools as the main use case.