A first metadata schema for learning analytics research data management
In most cases, research data builds the ground for scientific work and to gain new knowledge. Learning analytics is the science to improve learning in different fields of the educational sector. Even though it is a data-driven science, there is no research data management culture or concepts yet. A...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | DE EN |
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Verein Deutscher Bibliothekarinnen und Bibliothekare (VDB)
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/fb6da0fc300d47a78d4af40026aafdba |
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| Sumario: | In most cases, research data builds the ground for scientific work and to gain new knowledge. Learning analytics is the science to improve learning in different fields of the educational sector. Even though it is a data-driven science, there is no research data management culture or concepts yet. As every research discipline, learning analytics has its own characteristics, which are important for the creation of research data management concepts, in particular for generalization of data and modeling of a metadata model. The following work presents our results of a requirements analysis for learning analytics, in order to identify relevant elements for a metadata schema. To reach this goal, we conducted a literature survey followed by an analysis of our own research about frameworks for evaluation of collaborative programming scenarios from two universities. With these results, we present a discipline-specific scientific workflow, as well as a subject-specific object model, which lists all required characteristics for the development of a learning analytics specific metadata model for data repository usage.
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