SLC Catalyst: Learning About Causal Systems in Complex Domains

SLC Catalyst：了解复杂领域中的因果系统

• 批准号: 0350342
• 负责人: N Narayanan
• 金额: --
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-10-01 至 2007-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0350342&HistoricalAwards=false
• 关键词: SLC; Catalyst; Learning; About; Causal

Understanding causal systems is a significant aspect of learning in almost all disciplines of science, engineering and technology. However, understanding causal systems is difficult, and often leads to misconceptions because many aspects of such systems are dynamic, invisible, and interdependent. Complex causal domains can be characterized in a domain-independent and general manner in terms of structure and causal propagation of component behaviors in spatial and temporal dimensions. The need to comprehend and integrate structure and causal propagation in multiple dimensions may be the source of many of the difficulties that learners face in understanding, explaining, making predictions about, operating or troubleshooting systems in causal domains. Clearly, research under the rubric "Science of Learning" ought to address the topic of complex causal learning.At present, research on fundamental aspects of causal systems, mechanisms of causal understanding, ways to improve the learning and application of causal models, etc. remains fragmented, discipline-specific, and published in disparate forums. As a result, a global view of the state-of-research and research avenues to advance our understanding of learning about complex causal systems is simply not available. Therefore, we propose to undertake a research synthesis effort. Its goals are twofold: developing a prospective synthesis of the state-of-research on complex causal learning across disciplines, and identifying gaps in the knowledge base that past research has built up. This will be an effort that draws from multiple disciplines, including but not limited to cognitive science, computer science, engineering education, machine learning, psychology, and science education. The outcomes of this catalyst project will be a workshop on complex causal learning and a comprehensive research report on the topic.The intellectual merit of this project lies in its development and dissemination of a comprehensive research report on the current knowledge base on complex causal learning, a topic of interest and importance to K-16 education and learning research in several scientific and technical disciplines. Such a reference resource is at present not available to researchers and educators. The broad impacts of this project will be in raising awareness and focusing the attention of the learning research community on the topic of complex causal learning, and in cross-disciplinary learning research that the dissemination of our research report can potentially encourage. The results of this project, especially the research challenges it identifies, are expected to form the basis of a future proposal for a Science of Learning Center on complex causal learning.

了解因果系统是几乎所有科学，工程和技术学科学习的重要方面。但是，理解因果系统很困难，并且通常会导致误解，因为此类系统的许多方面都是动态的，无形的和相互依存的。复杂的因果域可以以域的独立和一般方式以空间和时间维度的组成行为的结构和因果传播来表征。在多个维度上理解和整合结构和因果传播的需求可能是学习者在理解，解释，对因果领域中的操作或故障排除系统中所面临的许多困难的根源。显然，在标题“学习科学”下的研究应该解决复杂因果学习的主题。在当前，有关因果系统基本方面的研究，因果理解的机制，改善因果模型的学习和应用的方法等。保持分散，特定于纪律，并在不同的论坛上出版。结果，根本没有对我们对学习复杂因果系统学习的理解的全球研究和研究途径的全球视图。因此，我们建议进行研究综合工作。它的目标是双重的：建立了对跨学科的复杂因果学习研究的前瞻性综合，并确定过去研究所建立的知识基础上的差距。这将是从多个学科中获取的努力，包括但不限于认知科学，计算机科学，工程教育，机器学习，心理学和科学教育。该催化剂项目的结果将是关于复杂因果学习和有关该主题的全面研究报告的研讨会。该项目的智力优点在于其发展和传播有关当前有关复杂因果学习的知识基础的全面研究报告，对于K-16的多个科学和技术学科中的K-16教育和学习研究的关注和重要性的话题。目前，研究人员和教育工作者无法获得这样的参考资源。该项目的广泛影响将是提高认识，并将学习研究界的注意力集中在复杂的因果学习主题上，以及我们研究报告的传播可能会鼓励的跨学科学习研究中。该项目的结果，尤其是其确定的研究挑战，有望构成对复杂因果学习中心学习中心的未来建议的基础。