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<p>Smart classrooms leverage advanced technologies to create interactive, data-driven learning environments that enhance educational experiences. However, interoperability remains a significant challenge, as many of these systems operate independently. This paper presents the Smart Classroom Ontology (SClassO), the very first holistic model designed to unify the key components of smart classroom environments, including people, context, and resources. By providing a semantic framework, SClassO enables seamless integration across heterogeneous systems, bridging gaps between sensing devices, learning management platforms, and classroom analytics. A proof of concept implementation demonstrates its ability to collect and integrate real-world data from both sensor devices and information systems using standard protocols such as MQTT and RESTful APIs. Data is stored both in OWL/RDF format and in a relational database to evaluate performance trade-offs. Finally, the study explores key architectural challenges, focusing on storage capacity, system performance, security, and data privacy.</p>

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Smart classroom ontology: enhancing interoperability in learning environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月9日

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  • Elena Figueroa,
  • Edgar Batista,
  • Tania Molero-Aranda,
  • Maria Ferre,
  • Antoni Martínez-Ballesté

摘要

Smart classrooms leverage advanced technologies to create interactive, data-driven learning environments that enhance educational experiences. However, interoperability remains a significant challenge, as many of these systems operate independently. This paper presents the Smart Classroom Ontology (SClassO), the very first holistic model designed to unify the key components of smart classroom environments, including people, context, and resources. By providing a semantic framework, SClassO enables seamless integration across heterogeneous systems, bridging gaps between sensing devices, learning management platforms, and classroom analytics. A proof of concept implementation demonstrates its ability to collect and integrate real-world data from both sensor devices and information systems using standard protocols such as MQTT and RESTful APIs. Data is stored both in OWL/RDF format and in a relational database to evaluate performance trade-offs. Finally, the study explores key architectural challenges, focusing on storage capacity, system performance, security, and data privacy.