Collaborative Research: Learning Linkages: Integrating Data Streams of Multiple Modalities and Timescales

协作研究：学习联系：整合多种模式和时间尺度的数据流

• 批准号: 1418181
• 负责人: John Stamper
• 金额: $ 23.54万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1418181&HistoricalAwards=false
• 关键词: Collaborative; Research; Learning; Linkages; Integrating; Collaborative; Research; Learning; Linkages; Integrating

This Research on Education and Learning (REAL) project arises from an October 2014 Ideas Lab on Data-intensive Research to Improve Teaching and Learning. The intentions of that effort were to (1) bring together researchers from across disciplines to foster novel, transformative, multidisciplinary approaches to using the data in large education-related data sets to create actionable knowledge for improving STEM teaching and learning environments in the medium term; and (2) revolutionize learning in the longer term. In this project, researchers from Carnegie-Mellon University, Wested, Arizona State University, and Northwestern University will collaborate to enhance understanding of influences on learning, and improve teaching and learning in high school and middle school STEM classes. To accomplish this, they will leverage the latest tools for data processing and many different streams of data that can be collected in technology-rich classrooms to (1) identify classroom factors that affect learning and (2) explore how to use that data to automatically track development of students' understanding and capabilities over time. Two forces are poised to transform research on learning. First, more and more student work is conducted on computers and online, producing vast amounts of learning-related data. At the same time, advances in computing, data mining, and learning analytics are providing new tools for the collection, analysis, and representation of these data. Together, the available data and analytical tools enable smart and responsive systems that personalize learning experiences for individual learners. The PIs aim to collect highly enriched data that go far beyond typical computer data capture, leveraging the latest tools for data processing to generate new insights about STEM teaching and learning. Working to maximize the potential while mitigating the risks of automated data collection and analysis, they will: (1) collect and integrate diverse sources of data including log files, videos, and written artifacts from across eight different two-week enactments of two different computer supported learning environments (one used in middle school math and one in high school science); and (2) compare analyses of log-file data with analyses of integrated datasets to understand the possibilities and limitations in using log-file data for assessment of student learning and proficiency. The collaborators expect their findings will inform both theories and practical recommendations applicable across a wide range of disciplines and settings.

这项关于教育与学习（REAL）项目的研究源于2014年10月的有关数据密集型研究的想法实验室，以改善教学。这种努力的目的是（1）将研究人员从跨学科中汇集在一起​​，以培养新颖的，变革性的多学科方法，以在与大型教育相关的数据集中使用数据，以创建可行的知识，以改善中等学期的STEM教学和学习环境； （2）从长远来看革新学习。在这个项目中，卡内基 - 梅隆大学，西部，亚利桑那州立大学和西北大学的研究人员将合作，以增强对学习影响的理解，并改善高中和中学STEM课程的教学和学习。 为此，他们将利用最新的工具进行数据处理，以及可以在技术丰富的教室中收集的许多不同数据流来（1）确定影响学习的课堂因素，以及（2）探索如何使用该数据来自动跟踪学生随着时间的推移的理解和能力的发展。两种力量有望改变学习研究。首先，越来越多的学生工作在计算机和在线上进行，产生了大量与学习相关的数据。同时，计算，数据挖掘和学习分析的进步正在为这些数据的收集，分析和表示提供新的工具。可用的数据和分析工具共同实现了为个体学习者个性化学习经验的智能和响应式系统。 PI的目的是收集远远超出典型计算机数据捕获的高度丰富数据，利用最新的工具用于数据处理，以生成有关STEM教学的新见解。致力于最大程度地发挥潜力，同时减轻自动数据收集和分析的风险，他们将：（1）收集和整合来自两个不同计算机支持的学习环境的八个不同的两周不同的两周颁布的数据来源，包括日志文件，视频和书面文物（一种用于中学数学和一个用于中学科学的一项）； （2）将日志文件数据的分析与集成数据集的分析进行比较，以了解使用日志文件数据来评估学生学习和熟练程度的可能性和局限性。合作者希望他们的发现将为理论和实用建议提供信息，适用于广泛的学科和设置。