EXP: Local Ground: A Contextually Grounded Approach for Learning Data Science Skills

EXP：本地基础：学习数据科学技能的基于上下文的方法

• 批准号: 1319849
• 负责人: David Bamman
• 金额: $ 55万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319849&HistoricalAwards=false
• 关键词: EXP; Local; Ground; Contextually; Grounded

In this Cyberlearning: Transforming Education Exploration project, researchers focus on the challenge of helping students in grades 4 through 6 develop data science skills -- understanding the significance of data and where it comes from, and developing capabilities involved in manipulating data and using it to draw conclusions and make informed decisions. The researchers are developing and refining software, called Local Ground, designed to allow learners to collect data relevant to local scientific or socio-scientific challenges and to help learners manipulate those data and use them to achieve the challenge, in the process developing charts, graphs, and narratives to be shared with others. The system is designed to provide scaffolding that helps students iteratively develop and refine representations and understandings of what their data represent. It is designed to display and translate between a variety of distinct representations. The intention is that Local Ground will act as an "auxiliary stimulus" -- a cultural form wedged between students' naïve ways of thinking and spontaneous inclinations to represent that thinking. The research team is using use of Local Ground as a context for investigating fundamental questions associated with developing data science skills, including how children convert naïve representations into more usable and communicable forms, how and if those forms are appropriate by others, how those processes support understanding of core mathematic, statistical, and computational constructs, and the impact of locally collected and relevant data on learning these competencies.Understanding, manipulating, and using data are essential skills for 21st century citizens. The researchers in this project are exploring a new approach to helping pre-teens begin to develop data skills and designing a software platform called Local Ground that supports 4th through 6th graders as they work on community projects they care about that require significant data collection, manipulation, analysis, and application. The pedagogical approach has students using data in sophisticated ways to address issues of importance to their communities; the tool helps them successfully gather, analyze, and use the data. In this context, the researchers are engaging in research that will add to what is known about how to help young learners understand core mathematical, statistical, and computational constructs important to data use and analysis.

在这个网络学习：转变教育探索项目中，研究人员专注于帮助四年级到六年级的学生发展数据科学技能的挑战--理解数据的重要性和数据来自哪里，并培养处理数据并利用数据得出结论和做出明智决策的能力。研究人员正在开发和完善名为Local Ground的软件，旨在允许学习者收集与当地科学或社会科学挑战相关的数据，并帮助学习者操纵这些数据并使用它们来完成挑战，在此过程中开发图表、图表和叙事与他人共享。该系统旨在提供脚手架，帮助学生迭代地开发和完善他们的数据所代表的表示和理解。它被设计为在各种不同的表示之间显示和转换。其意图是，当地将充当一种“辅助刺激”--一种夹在学生天真的思维方式和代表这种思维的自发倾向之间的文化形式。研究小组正在使用本地基础作为一个背景来调查与发展数据科学技能相关的基本问题，包括儿童如何将幼稚的表示转换为更有用和更容易交流的形式，这些形式如何以及是否适合其他人，这些过程如何支持对核心数学、统计和计算结构的理解，以及本地收集的相关数据对学习这些能力的影响。理解、操作和使用数据是21世纪公民的基本技能。该项目的研究人员正在探索一种新的方法来帮助青少年开始发展数据技能，并设计一个名为Local Ground的软件平台，支持4到6年级的学生在他们关心的社区项目中工作，这些项目需要大量的数据收集、处理、分析和应用。教学方法让学生以复杂的方式使用数据来解决对他们社区重要的问题；该工具帮助他们成功地收集、分析和使用数据。在这种背景下，研究人员正在进行的研究将增加有关如何帮助年轻学习者理解对数据使用和分析至关重要的核心数学、统计和计算结构的已知知识。