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

第七届国际教育数据挖掘会议(EDM)是一系列年度国际会议中的第七次，这些会议致力于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学习的环境。它的青年研究人员轨道将年轻的研究人员与教育管理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。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NSF培养更多科学、技术、工程和数学方面的高级专业人员的使命。EDM社区的独特之处在于它综合和交叉了三种与STEM教育网络学习相关的研究能力：(1)开发分析细粒度教育数据的方法，(2)评估尖端学习技术，以及(3)利用从这些分析中获得的见解来完善认知科学、社会科学和学习科学中提出的学习理论。会议平衡了理论基础、对细粒度教育数据的分析、将研究结果应用于学习环境设计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从不同角度了解教育数据挖掘所涉及的方法和技术以及如何利用这些方法和技术取得最佳效果；如何利用现有数据的分析为教育和学习技术的设计提供信息；以及如何利用这些数据促进对学习过程的理解。NSF的资金将用于支付美国大学高级研究生的部分旅行和住宿费用。

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.