• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE Frontiers in Education Conference >The Integration of Data Analytics to Assess Multi-Complex Environments of Research to Practices in Engineering Education
【24h】

The Integration of Data Analytics to Assess Multi-Complex Environments of Research to Practices in Engineering Education

机译：集成数据分析以评估工程教育实践中研究的多种复杂环境

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The integration of data analytics in engineering education to address technical requirements from a multi-complex environment perspective concept will explore areas of research to practices category in regards to the current work in progress using data analytics tools (e.g., IBM Watson Analytics). The results obtained from a multi-complex environment have aided students and improved their decision approach to quantify data accuracy and project requirements in education practices for predictive learning. In using the data sets developed from Watson Analytics, this assembly of display in multi-complex environments provided students with the ability to assess and understand the visual presentation to determine predictive models in data exploration. Data exploration was used to identify a research approach in the education assessment of the multi-complex environments of engineering students' projects. The multi-complex environments and the variables assessment also provided insight with an understanding of project requirements and objectives using data visualization techniques and decision relationships gained from data exploration. This approach investigated the learning methods and decision practices through pattern recognition, educational objectives and course outcomes in specific multi-complex environments with efforts supporting research to practices. The integration of analytics tools with regard to decision-based learning allowed the engineering students the ability to forecast requirements and create new methods critical to their engineering design. This was significant due to the students' ability to model decisions in a manner that experts had challenged engineering education using research to practices to address aspects of the multi-complex environments based on industry standards. This technique had also improved the practical implication for student learning and the decision methods to support research in engineering education with regard to predictive learning and modeling design methods.
机译：将数据分析集成到工程教育中，以便从多复杂环境的角度解决技术需求，这将使用数据分析工具（例如IBM Watson Analytics）针对当前正在进行的工作探索研究领域到实践类别。从复杂的环境中获得的结果帮助了学生，并改善了他们的决策方法，从而量化了预测学习的教育实践中的数据准确性和项目要求。在使用从Watson Analytics开发的数据集时，在复杂的环境中进行的这种显示组合使学生能够评估和理解视觉表示，从而确定数据探索中的预测模型。数据探索被用于在工程学生项目的复杂环境的教育评估中确定一种研究方法。使用数据可视化技术和从数据探索中获得的决策关系，多复杂的环境和变量评估还提供了对项目需求和目标的理解的洞察力。该方法通过在特定的复杂环境中通过模式识别，教育目标和课程结果来研究学习方法和决策实践，并努力支持对实践的研究。与基于决策的学习相关的分析工具的集成使工程专业的学生能够预测需求并创建对其工程设计至关重要的新方法。之所以如此重要，是因为学生具有以决策专家对工程教育进行挑战的方式来建模决策的能力，而工程教育则采用了针对行业的研究方法来解决基于行业标准的复杂环境。该技术还改善了对学生学习的实际含义，并为预测学习和建模设计方法方面的决策方法提供了支持，以支持工程教育中的研究。

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