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.

在几乎所有科学、工程和技术学科中，理解因果系统是学习的一个重要方面。然而，理解因果系统是困难的，并且经常导致误解，因为这些系统的许多方面是动态的、不可见的和相互依赖的。复杂的因果域可以在空间和时间维度上以独立于域的和一般的方式来表征。在多个维度上理解和整合结构和因果传播的需要可能是学习者在理解、解释、预测、操作或排除因果领域中的系统时面临的许多困难的来源。显然，在“学习科学”的标题下的研究应该解决复杂因果学习的主题。目前，关于因果系统的基本方面、因果理解的机制、改进因果模型学习和应用的方法等方面的研究仍然是碎片化的、学科化的，并且发表在不同的论坛上。因此，对于研究现状和研究途径的全局视图，以促进我们对复杂因果系统学习的理解，根本无法获得。因此，我们建议进行一项综合研究工作。它的目标有两个：对跨学科复杂因果学习的研究现状进行前瞻性综合，并确定过去研究建立的知识库中的空白。这将是一项涉及多个学科的工作，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。这个催化剂项目的成果将是一个关于复杂因果学习的讲习班和一份关于该主题的综合研究报告。这个项目的智力价值在于它开发和传播了一份关于当前复杂因果学习知识库的综合研究报告，这是一个对K-16教育和几个科学和技术学科的学习研究感兴趣和重要的话题。这样的参考资源目前还没有提供给研究人员和教育工作者。这个项目的广泛影响将是提高学习研究界对复杂因果学习主题的认识和关注，以及我们研究报告的传播可能会鼓励的跨学科学习研究。这个项目的结果，特别是它确定的研究挑战，有望成为未来关于复杂因果学习的学习科学中心提案的基础。