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Título
Validation of the learning ecosystem metamodel using transformation rules
Autor(es)
Materia
Metamodel
Information technology
Model Driven Development
learning ecosystems
information systems
software engineering
Ecore
software ecosystems
technological ecosystems
Fecha de publicación
2018
Citación
García-Holgado, A., & García-Peñalvo, F. J. (2019). Validation of the learning ecosystem metamodel using transformation rules. Future Generation Computer Systems, 91, 300-310. doi:10.1016/j.future.2018.09.011
Resumen
The learning ecosystem metamodel is a platform-independent model to define learning ecosystems. It is
based on the architectural pattern for learning ecosystems. To ensure the quality of the learning ecosystem
metamodel is necessary to validate it through a Model-to-Model transformation. Specifically, it is required to
verify that the learning ecosystem metamodel allows defining real learning ecosystems based on the
architectural pattern. Although this transformation can be done manually, the use of tools to automate the
process ensures its validity and minimize the risk of bias. This work describes the validations process
composed of eight phases and the results obtained, in particular: the transformation of the MOF metamodel
to Ecore to use stable tools for the validation, the definition of a platform-specific metamodel for defining
learning ecosystems and the transformation from instances of the learning ecosystem metamodel to
instances of the platform-specific metamodel using ATL. A quality framework has been applied to the three
metamodels involved in the process to guarantee the quality of the results. Furthermore, some phases have
been used to review and improve the learning ecosystem metamodel in Ecore. Finally, the result of the
process demonstrates that the learning ecosystem metamodel is valid. Namely, it allows defining models
that represent learning ecosystems based on the architectural pattern that can be deployed in real contexts
to solve learning and knowledge management problems
URI
DOI
10.1016/j.future.2018.09.011
Colecciones
- GRIAL. Artículos [441]