{"title":"可视化分析支持教育","authors":"K. Börner","doi":"10.1145/2330601.2330604","DOIUrl":null,"url":null,"abstract":"The amount of data about us and our world is increasing rapidly, and the capability to analyze large data sets---so-called big data---becomes a key basis of competition, underpinning new waves of productivity growth and innovation. The big data phenomenon is fueled by cheap sensors and high-throughput simulation models, the increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet. It exists from social media to cell biology offering unparalleled opportunities to document the inner workings of many complex systems [1]. Research by MGI and McKinsey's Business Technology Office argues that there will be a shortage of talent necessary for organizations to take advantage of big data. \"By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions\" [2]. In everyday life, people deal with large amounts of data regularly: online search engines provide access to millions of web sites almost instantly; consumer sites offer literally thousands of purchase options seamlessly; and social media sites let you create and benefit from extensive social networks. In bestselling books like Freakonomics, Super Crunchers and The Numerati, authors illuminate how more and more decisions in health care, politics, education, and other sectors utilize big data and data analysis [3]. The texts highlight the growing need for specialists and every-day citizens to be able to understand and interpret data. Whether it is a table of nutritional information, a graph of stock prices, or a chart comparing health care plans, the skills of understanding and interpreting data are necessary to navigate successfully through daily life. This talk starts with a review of visual analytics projects that aim to increase our understanding of how people learn, increase the efficacy of learning environments, or support decision making in education [4]. The second part of the talk provides a theoretical framework for the design of effective data analysis workflows and insightful visualizations. It also introduces plug-and-play macroscope tools [5], see also http://cishell.org, that were designed for different research communities and are used by more than 120,000 users from 40+ countries to design and benefit from visualizations of complex data. The talk concludes with a discussion of challenges that arise when visual analytics tools are introduced to classrooms and informal science education.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"114 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Visual analytics in support of education\",\"authors\":\"K. Börner\",\"doi\":\"10.1145/2330601.2330604\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The amount of data about us and our world is increasing rapidly, and the capability to analyze large data sets---so-called big data---becomes a key basis of competition, underpinning new waves of productivity growth and innovation. The big data phenomenon is fueled by cheap sensors and high-throughput simulation models, the increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet. It exists from social media to cell biology offering unparalleled opportunities to document the inner workings of many complex systems [1]. Research by MGI and McKinsey's Business Technology Office argues that there will be a shortage of talent necessary for organizations to take advantage of big data. \\\"By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions\\\" [2]. In everyday life, people deal with large amounts of data regularly: online search engines provide access to millions of web sites almost instantly; consumer sites offer literally thousands of purchase options seamlessly; and social media sites let you create and benefit from extensive social networks. In bestselling books like Freakonomics, Super Crunchers and The Numerati, authors illuminate how more and more decisions in health care, politics, education, and other sectors utilize big data and data analysis [3]. The texts highlight the growing need for specialists and every-day citizens to be able to understand and interpret data. Whether it is a table of nutritional information, a graph of stock prices, or a chart comparing health care plans, the skills of understanding and interpreting data are necessary to navigate successfully through daily life. This talk starts with a review of visual analytics projects that aim to increase our understanding of how people learn, increase the efficacy of learning environments, or support decision making in education [4]. The second part of the talk provides a theoretical framework for the design of effective data analysis workflows and insightful visualizations. It also introduces plug-and-play macroscope tools [5], see also http://cishell.org, that were designed for different research communities and are used by more than 120,000 users from 40+ countries to design and benefit from visualizations of complex data. 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引用次数: 6
摘要
关于我们和我们的世界的数据量正在迅速增加,分析大数据集(所谓的大数据)的能力成为竞争的关键基础,支撑着新一波生产力增长和创新。廉价的传感器和高吞吐量的仿真模型、企业获取的信息量和细节的增加、多媒体、社交媒体和互联网的兴起,推动了大数据现象的发展。它存在于从社交媒体到细胞生物学,为记录许多复杂系统的内部运作提供了无与伦比的机会[1]。麦肯锡全球研究院(MGI)和麦肯锡商业技术办公室(McKinsey’s Business Technology Office)的研究认为,企业利用大数据所需的人才将会短缺。“到2018年,仅美国就可能面临14万至19万具有深度分析技能的人才短缺,以及150万能够利用大数据分析做出有效决策的管理人员和分析师”[2]。在日常生活中,人们经常处理大量的数据:在线搜索引擎几乎可以即时访问数百万个网站;消费者网站提供了数千种无缝的购买选择;社交媒体网站可以让你创建和受益于广泛的社交网络。在《魔鬼经济学》(Freakonomics)、《超级计算器》(Super Crunchers)和《数字高手》(The Numerati)等畅销书中,作者阐明了越来越多的医疗、政治、教育和其他领域的决策如何利用大数据和数据分析[3]。这些文本强调了专家和普通公民日益增长的理解和解释数据的需求。无论是营养信息表,股票价格图表,还是比较医疗计划的图表,理解和解释数据的技能对于成功地度过日常生活都是必要的。本次演讲首先回顾了视觉分析项目,这些项目旨在提高我们对人们如何学习的理解,提高学习环境的效率,或支持教育决策[4]。演讲的第二部分为设计有效的数据分析工作流程和富有洞察力的可视化提供了理论框架。它还介绍了即插即用的宏观工具[5],参见http://cishell.org,这些工具是为不同的研究社区设计的,被来自40多个国家的120,000多名用户用于设计复杂数据的可视化并从中受益。讲座最后讨论了当可视化分析工具被引入课堂和非正式科学教育时所面临的挑战。
The amount of data about us and our world is increasing rapidly, and the capability to analyze large data sets---so-called big data---becomes a key basis of competition, underpinning new waves of productivity growth and innovation. The big data phenomenon is fueled by cheap sensors and high-throughput simulation models, the increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet. It exists from social media to cell biology offering unparalleled opportunities to document the inner workings of many complex systems [1]. Research by MGI and McKinsey's Business Technology Office argues that there will be a shortage of talent necessary for organizations to take advantage of big data. "By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions" [2]. In everyday life, people deal with large amounts of data regularly: online search engines provide access to millions of web sites almost instantly; consumer sites offer literally thousands of purchase options seamlessly; and social media sites let you create and benefit from extensive social networks. In bestselling books like Freakonomics, Super Crunchers and The Numerati, authors illuminate how more and more decisions in health care, politics, education, and other sectors utilize big data and data analysis [3]. The texts highlight the growing need for specialists and every-day citizens to be able to understand and interpret data. Whether it is a table of nutritional information, a graph of stock prices, or a chart comparing health care plans, the skills of understanding and interpreting data are necessary to navigate successfully through daily life. This talk starts with a review of visual analytics projects that aim to increase our understanding of how people learn, increase the efficacy of learning environments, or support decision making in education [4]. The second part of the talk provides a theoretical framework for the design of effective data analysis workflows and insightful visualizations. It also introduces plug-and-play macroscope tools [5], see also http://cishell.org, that were designed for different research communities and are used by more than 120,000 users from 40+ countries to design and benefit from visualizations of complex data. The talk concludes with a discussion of challenges that arise when visual analytics tools are introduced to classrooms and informal science education.