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Laboratoire Interdisciplinaire de l’Université Française d’Egypte (UFEID Lab), Université Française d’Egypte, 21 Ismailia Desert Road Shorouk City, Cairo 11837, Egypt

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1 Papers

Managing Data Heterogeneity for Ontology-Driven Models: Application to Gamified E-Learning Contexts

Topics: Recommender systems in educational domain; Educational knowledge management and ontologies ; Educational linked data and semantic web ; Knowledge acquisition, extraction and reuse in education ; Evaluating and improving teachers' support

Authors: Yara Gomaa ^{1

;

2} , Christine Lahoud ³ , Marie-Hélène Abel ¹ and Sherin Moussa ^{2

;

4}

Affiliations: ¹ Laboratoire HEUDIASYC, Université de Technologie de Compiègne, Sorbonne Universités, 57 Avenue Landshut, Compiègne 60200, France , ² Laboratoire Interdisciplinaire de l’Université Française d’Egypte (UFEID Lab), Université Française d’Egypte, 21 Ismailia Desert Road Shorouk City, Cairo 11837, Egypt , ³ CIAD UR 7533,Univ. BourgogneFranche-Comté, UTBM, F-90010 Belfort, France , ⁴ Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

Digital Object Identifier: 10.5220/0013496700003932

Proceedings of EKM 2025, ISBN: 978-989-758-746-7

Quick Abstract

Data heterogeneity within gamified e-learning systems exposes a challenge for ontology-driven models, specifically ontology-based recommender systems. These systems can help teachers who are unfamiliar with gamification by offering personalized recommendations to gamify their pedagogical resources. Yet, developing such systems requires collecting and integrating diverse data about users, resources, and game elements, originating from multiple sources, like learning management systems and educational repositories, each with varying formats and inconsistent semantics. This paper proposes an approach to manage the complexities of collecting and preparing heterogeneous data for an ontology-driven model within gamified e-learning contexts. A full overview is provided on the data workflow, which consists of two main phases: (1) Data collection, which combines automated techniques through APIs and web scraping, and (2) Data Integration by means of mapping the collected data into our Teacher in Gamified e-learning Context (TGC) ontology to produce coherent and semantically enriched structure. The resulting data repository facilitates semantic queries, inference, and knowledge enrichment, overcoming challenges like cold-start scenarios and supporting the dynamic generation of personalized recommendations. This proposed approach aims to establish a robust approach that addresses the challenges of data heterogeneity, ensuring consistent and meaningful integration for ontology-based recommender systems in gamified e-learning contexts.