CAP: Data Consortium Fellows: A Mentorship Program to Expand the Cyberlearning Data Analytics Community

网络学习和未来学习技术计划资助的努力将有助于展望下一代学习技术，并促进我们对人们在技术丰富的环境中如何学习的了解。能力建设(CAP)项目提高了研究人员理解未来应如何设计和使用这种技术的能力，并支持新的能力，使研究人员能够回答有关人们如何学习、如何促进或评估学习、和/或如何为学习而设计的问题。拟议的工作是一系列峰会，旨在通过接触和指导先进的数据分析技术来提高学习科学界研究人员的能力。它解决了两个新兴研究领域交叉的关键需求，并推进了CAP项目的更大目标。该项目是一系列峰会，建立在以前资助的NSF工作的基础上，以促进学习科学。学习科学界的成员往往没有意识到数据分析界可能提高其工作的深度和影响力的补充性发展。这些峰会的参与者是来自不同项目的当前NSF奖获得者，他们的工作重点是学习科学。通过数据分析方面的曝光和指导进行建设，不仅扩大了这两个领域的交界处的知识体系，还巧妙地利用了当前NSF资助的项目。

The Cyberlearning and Future Learning Technologies Program funds efforts that will help envision the next generation of learning technologies and advance what we know about how people learn in technology-rich environments. Capacity-building (CAP) projects increase the ability of researchers to understand how such technology should be designed and used in the future and supports new capacity in allowing researchers to answer questions about how people learn, how to foster or assess learning, and/or how to design for learning. The proposed work is a series of summits designed to increase the capacity of researchers in the learning sciences community through exposure and instruction in advanced data analytic techniques. It addresses a critical need at the intersection of two burgeoning fields of study and advances the larger goal of CAP projects.This project is a series of summits that builds on previously funded NSF work to advance learning sciences. Members of the learning science community are often unaware of the complementary developments in the data analytics community that could enhance the depth and impact of their work. The participants in these summits are current NSF awardees, from various programs, whose work focuses on learning sciences. Building through exposure and instruction in data analytics not only expands the body of knowledge at the interface of these two fields, it smartly leverages current NSF funded projects.