Abstract
Open Educational Resources include different types of material for learning and teaching. Over time the number of them has been increasing to a great extent. Although the availability of educational material is beneficial for teachers and learners; however, the search for relevant material becomes a complex task due to the limited availability of specialized tools to locate content that meets the learners’ level of knowledge. The current research presents a recommendation service that provides a learning path based on Open Educational Resources. The learning path is created according to the topic of interest of users, and the level of understanding that they have about a particular topic. The recommendation method is based on a knowledge graph, that is created based on the metadata of educational resources obtained from an academic repository. Then, the graph is enriched by three methods: 1) keyword reconciliation using Spanish DBPedia as a target, 2) semantic annotation to find semantic resources, and 3) identification of the level of knowledge of each OER associated with a particular topic. The enriched graph is stored in GraphDB, a repository that provides the creation of semantic similarity indexes to generate recommendations. Results are compared with the TF-IDF measure and validated with the precision metric.
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The authors thank the Computer Science Department of Universidad Técnica Particular de Loja of Ecuador for sponsoring this academic project.
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Yaguana, J., Chicaiza, J. (2023). Recommendation of Learning Paths Based on Open Educational Resources. In: Ortiz-Rodriguez, F., Villazón-Terrazas, B., Tiwari, S., Bobed, C. (eds) Knowledge Graphs and Semantic Web. KGSWC 2023. Lecture Notes in Computer Science, vol 14382. Springer, Cham. https://doi.org/10.1007/978-3-031-47745-4_5
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