Synthesis and design workshop: Research Priorities in Learning Analytics

综合与设计研讨会：学习分析的研究重点

• 批准号: 1824998
• 负责人: Stephanie Teasley
• 金额: $ 10万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1824998&HistoricalAwards=false
• 关键词: Synthesis; design; workshop; Research; Priorities

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.

该研讨会是通过“亲爱的同事信：数字科学，技术，工程和数学（STEM）学习环境的设计原则（NSF 18-017）”资助的。“高等教育机构正受到三大转变的冲击：学生需要和想要的能力的性质发生变化，他们所服务的人口结构发生变化，以及用于建立、衡量和交流能力和专业知识的技术和战略发生变化。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，使人们有可能想象设计学习环境，使学习更有成效，更实惠，更优雅地适应个人的一生。 学习分析的新领域在理解教育大数据如何产生见解以改善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，一些关键问题仍然没有答案。本次研讨会的目的是研究学习分析在促进技术学习环境进步中的作用。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重点。 一个中心主题将是探索使用数据科学中强大工具的新方法（机器学习，社交网络分析，复杂数据的分析和可视化，时间，多尺度和统计模型，异构数据的整合，数据清洗，争吵和出处跟踪，数据隐私和网络安全）来定义能力，测量它，并使用丰富的新方法来研究学习。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中面临的最关键挑战。研讨会的成果将是对学习分析中最优先的研究需求的明确定义，以及指导这些领域公共和私人研究支持的实用路线图。由此产生的白色文件将提供见解，以改善广泛的学习环境的背景下，在中学后教育。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。