How OERs Succeed with the Learning Objectives Matrix (LZM) in University Collections
Year: 2025
Type: article
Abstract: SODa strengthens the data literacy and data science skills of all staff involved in university collections. The focus is on OERs for collection-related research data management, providing foundational knowledge, discipline-specific content, current standards, and best practices. The development of these OERs follows a didactic, methodological, and technical framework. Coherent personas serve as the foundation for targeted learning design, making learners' prior knowledge, needs, motivation, and experience transparent. Research-based learning fosters independence and personal responsibility. In this context, the learning objectives matrix for RDM is applied and expanded as needed to incorporate additional discipline-specific aspects. The learning objectives set the direction, structure the learning process, and balance knowledge transfer with self-directed learning. They define the use of didactic methods and formats, ensure learning success, and support the practical application of knowledge. Accordingly, problem-based learning approaches are integrated into both synchronous and asynchronous formats and made available as FAIR OERs to the collection and RDM community. Currently, planned modules include collection development, cataloging, working with data in conservation and restoration documentation, semantic data modeling, data visualization, and image classification. This project with the funding code 16DKZ2016B was funded by the Federal Ministry of Education and Research (BMBF) and NextGenerationEU (NGEU).
Authors: Hastik, Canan, Schwenk, Gudrun Angelika, Reichert, Rebekka
Institution: Humboldt-Universität zu Berlin
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W6967849878
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