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4 septembre 2024
- handle: 10670/1.l41yg8
- isbn: 979-8-4007-1094-0/24/09
- hal: hal-04654407
- doi: 10.1145/3677525.3678686
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info:eu-repo/semantics/altIdentifier/doi/10.1145/3677525.3678686
http://creativecommons.org/licenses/by/ , info:eu-repo/semantics/OpenAccess
Mots-clés
Knowledge graphs Graph Machine Learning Knowledge representation and reasoning Educational ResourcesSujets proches
Natural science Sciences Science of science Relations--Foreign countries Relations (Canon law)Citer ce document
Aymen Bazouzi et al., « Precedability Prediction Between Open Educational Resources », HALSHS : archive ouverte en Sciences de l’Homme et de la Société, ID : 10.1145/3677525.3678686
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Résumé
The abundance of Educational Resources (ERs) has allowed people to have access to a vast amount of knowledge. However, it can be difficult, for both educators and learners, to navigate through these resources. One way to facilitate navigation is to identify useful relations between these resources. This can improve the teaching and learning experiences by allowing the users to go from one resource to another based on the identified relations, such as precedence. In this work, we introduce the notion of precedability between educational resources; whether a resource A can precede another resource B. Then, we propose a two-step method to identify precedability relations between educational resources. Our method structures the educational resources in an enriched Knowledge Graph (KG). Then, it uses a Graph Neural Network (GNN) model to predict precedability relations. Our method performed better than multiple baselines on different benchmarks.