...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Future generation computer systems >Validation of the learning ecosystem metamodel using transformation rules
【24h】

Validation of the learning ecosystem metamodel using transformation rules

机译：使用转换规则验证学习生态系统元模型

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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. (C) 2018 Elsevier B.V. All rights reserved.
机译：学习生态系统元模型是用于定义学习生态系统的独立于平台的模型。它基于用于学习生态系统的架构模式。要确保学习型生态系统元模型的质量，必须通过模型到模型的转换对其进行验证。具体来说，需要验证学习生态系统元模型是否允许基于架构模式定义实际的学习生态系统。尽管此转换可以手动完成，但是使用工具使过程自动化可以确保其有效性并最大程度地降低偏差的风险。这项工作描述了由八个阶段组成的验证过程以及所获得的结果，尤其是：将MOF元模型转换为Ecore以使用稳定的工具进行验证，定义用于定义学习生态系统的特定于平台的元模型以及从使用ATL将学习生态系统元模型的实例转换为特定于平台的元模型的实例。质量框架已应用于过程中涉及的三个元模型，以确保结果的质量。此外，在Ecore中，某些阶段已用于审查和改进学习生态系统元模型。最后，该过程的结果表明学习型生态系统元模型是有效的。即，它允许基于可以在实际环境中部署的架构模式定义代表学习生态系统的模型，以解决学习和知识管理问题。 （C）2018 Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号