Pixel International Conferences

For two decades, learning object metadata standards have facilitated filtering, cataloging, and creating dependencies. Initially, cross-schema compatibility was minimal, as learning content was primarily managed within individual systems. However, recent years have seen a shift towards greater interoperability, particularly in the realm of open educational resources (OER), where sharing content across platforms has become crucial [1] [2]. The OWL-Learning platform, initiated in 2016, initially adopted the Learning Object Metadata Standard (LOM) but remained adaptable. Recognizing the growing importance of interoperability, especially with competing or complementary content in various learning management systems (LMS), the platform developed a metadata adaptor. This adaptor translates LOM Metadata into the MOOChub API Metadata scheme, enabling interoperability between platforms that use the same model or can translate their model into another standard. This development opens up possibilities for editors to create courses by compiling content from different platforms and arranging it in a pedagogically suitable order [3]. The crucial requirement is using the same metadata schemes or mapping tools that can adapt metadata from one scheme to another. The OWL platform's metadata adaptor, applying the MOOChub API Metadata scheme, facilitates such exchanges. Currently undergoing testing within an initiative by the German federal ministry of education and research, the OWL platform is part of a broader national education platform. This initiative aims to connect all educational sectors, from pre-school to continuing and further education. The middleware ensures a single sign-on solution for users, streamlining access and fostering collaboration. This paper provides insights into the history of metadata in educational contexts, outlines challenges and strengths of developments and implementations, and shares findings from initial tests connecting platforms and dealing with content across platform borders. The initiative signals a significant step towards creating a cohesive educational ecosystem that benefits both learners and providers.

References:

[1] Kimmons, R., Irvine, J. (2023). Future Directions in OER. In: Otto, D. et al. Distributed Learning Ecosystems. Concepts, Resources and Repositories (pp 183-199).

[2] Tavakoli, M. et al (2021). Matadata Analysis of Open Educational Resources. In: LAK21: 11^th International Learning Analytics and Knowledge Conference (pp 626-631).

[3] Digel, S. et al (2023). Enabling Individualized and Adaptive Learning – The Value of an AI-Based Recommender System for Users of Adult and Continuing Education Platforms. In: AIED 2023: Artificial Intelligence in Education. (pp 797-803).

Keywords:

Metadata, interoperability, distributed learning systems, OER