CAP: Student Travel Support for the 7th International Conference on Educational Data Mining (EDM 2014)

第七届教育数据挖掘国际会议（EDM）是一系列年度国际会议中的第七届会议，该会议关注的是开发分析来自教育环境的数据的方法，并使用这些方法更好地了解学生和他们的学习环境。它的青年研究人员专程将年轻的研究人员与EDM社区的资深人士聚集在一起，帮助他们拓宽对教育数据的看法，以及如何为评估目的挖掘和分析这些数据，并更多地了解人们是如何学习的。这些活动旨在帮助下一代网络学习和学习技术研究人员做好准备。这项活动支持了美国国家科学基金会培养更多科学、技术、工程和数学方面的高级专业人才的使命。EDM社区在三个STEM教育网络学习相关研究能力的综合和交叉发展方面是独一无二的：(1)开发分析细粒度教育数据的方法；(2)评估前沿学习技术；(3)利用从这些分析中获得的见解来完善认知、社会和学习科学中提出的学习理论。会议平衡了理论基础、细粒度教育数据的分析、研究结果在学习环境设计中的应用以及对学习环境的严格评估。青年研究人员跟踪活动旨在帮助青年研究人员获得关于教育数据挖掘所涉及的方法和技术的各种观点，以及如何使用它们达到最佳效果；可用数据分析可用于教育和学习技术设计的方法；这些数据可以用来促进对潜在学习过程的理解。美国国家科学基金会的资金将用于支持来自美国大学的高级研究生的一些旅行和住宿费用。

The 7th International Conference on Educational Data Mining (EDM) is the 7th in a series of annual international conferences concerned with developing methods for analyzing the data that come from educational settings and using those methods to better understand students and the settings in which they learn. Its Young Researcher Track brings young researchers together with senior people in the EDM community to help them broaden their perspectives on educational data and how to mine and analyze it for assessment purposes and to learn more about how people learn. Activities are designed to help prepare the next generation of cyberlearning and learning technologies researchers. This activity supports the mission of NSF to train more advanced professionals in Science, Technology, Engineering, and Mathematics. The EDM community is unique in its synthesis and cross-fertilization of three STEM education cyberlearning-related research capacities: (1) developing methods for analysis of fine-grained educational data, (2) evaluating cutting-edge learning technologies, and (3) using insights gained from these analyses to refine theories of learning proposed in the cognitive, social, and learning sciences. The conference balances theoretical grounding, analysis of fine-grained educational data, application of the findings to design of learning environments, and rigorous evaluation of the learning environments. Young Researcher Track activities are designed to help young researchers gain a variety of perspectives on the methods and technologies involved in educational data mining and how use them to best effect; the ways in which analysis of available data can be used to inform education and design of learning technologies; and the ways such data can be used to advance understanding of processes underlying learning. NSF funding will be used to support some of the cost of travel and accommodations for advanced graduate students from US universities.