Synthesis and design workshop: Research Priorities in Learning Analytics

本次研讨会由“亲爱的同事来信：数字科学、技术、工程和数学(STEM)学习环境的设计原则”(NSF 18-017)资助。高等教育机构正受到三个重大转变的冲击：学生需要和想要的能力性质的变化，他们所服务的人口结构的变化，以及可用于构建、衡量和交流能力和专业知识的技术和战略的变化。强大的新信息技术、前所未有的收集和分析大量数据的机会，以及对人们如何学习的新见解，使我们能够想象设计使学习更高效、更负担得起、更优雅地适应个人一生的学习环境。学习分析的新领域在理解教育大数据如何产生见解以改进课堂实践方面取得了重大进展。然而，在确保所有美国人接受在现代经济中繁荣所需的教育的重要领域，许多关键问题仍然没有得到回答。本研讨会的目的是调查学习分析在促进技术学习环境进步方面的作用。研讨会将汇集跨学科专家，阐述最先进的技术，并提出未来十年学习分析的研究优先事项。一个中心主题将是探索使用数据科学中强大的工具(机器学习、社会网络分析、复杂数据的分析和可视化、时间、多尺度和统计模型、不同类型数据的集成、数据清理、争论和来源追踪、数据隐私和网络安全)来定义、衡量能力的新方法，并使用丰富的新方法来研究学习来建立能力。研讨会还将探讨如何将这些工具的全部功能应用于学习分析所面临的最关键挑战。讲习班的成果将是对学习分析方面最优先的研究需求的明确定义，以及指导这些领域的公共和私人研究支助的实用路线图。由此产生的白皮书将为改善高等教育中广泛的学习环境提供见解。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This workshop is funded through the "Dear Colleague Letter: Principles for the Design of Digital Science, Technology, Engineering, and Mathematics (STEM) Learning Environments (NSF 18-017)." Post-secondary educational institutions are being buffeted by three major transitions: changes in the nature of the competencies students need and want, changes in the demographic mix of people that they serve, and changes in the technologies and strategies available to build, measure, and communicate competence and expertise. Powerful new information technologies, unprecedented opportunities to gather and analyze large volumes of data, and new insights into how people learn, make it possible to imagine designing learning environments that make learning more productive, more affordable, and more gracefully adaptable to individuals throughout their lifetimes. The new field of learning analytics has made major advances in understanding how educational Big Data can produce insights to improve classroom practices. Yet, a number of critical questions remain unanswered in important areas to ensure all Americans receive the education they need to prosper in a modern economy. The aim of this workshop is to investigate the role of learning analytics in contributing to advances in technological learning environments. The workshop will bring together interdisciplinary experts to articulate the state-of-the-art and propose research priorities for learning analytics in the coming decade. A central theme will be to explore new ways to use powerful tools in data science (machine learning, social network analysis, analytics and visualization of complex data, temporal, multi-scale and statistical models, integration of heterogeneous data, data scrubbing, wrangling and provenance tracking, data privacy and cybersecurity) to define competence, measure it, and build it using a rich array of new approaches to study learning. The workshop will also explore how the full power of these tools can be applied to the most critical challenges faced in learning analytics. The outcome of the workshop will be a clear definition of the highest priority research needs in learning analytics and a practical roadmap to guide public and private research support in these areas. The resulting white paper will provide insights to improve a wide range of learning environment contexts in in post-secondary education.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.