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

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.

这项教育与学习研究 (REAL) 项目源于 2014 年 10 月关于改善教学的数据密集型研究的创意实验室。这项工作的目的是 (1) 将跨学科的研究人员聚集在一起，培育新颖的、变革性的、多学科的方法，使用大型教育相关数据集中的数据来创建可操作的知识，以在中期改善 STEM 教学和学习环境; (2) 从长远来看，彻底改变学习方式。在这个项目中，来自卡内基梅隆大学、韦斯特德大学、亚利桑那州立大学和西北大学的研究人员将合作，加深对学习影响的理解，并改善高中和初中 STEM 课程的教学。 为了实现这一目标，他们将利用最新的数据处理工具和可以在技术丰富的教室中收集的许多不同的数据流，以（1）识别影响学习的课堂因素，以及（2）探索如何使用该数据自动跟踪学生的理解和能力随时间的发展。有两种力量即将改变学习研究。首先，越来越多的学生作业在计算机和在线上进行，产生大量与学习相关的数据。与此同时，计算、数据挖掘和学习分析的进步为这些数据的收集、分析和表示提供了新的工具。可用的数据和分析工具共同支持智能且响应迅速的系统，为个体学习者提供个性化的学习体验。 PI 的目标是收集远远超出典型计算机数据捕获范围的高度丰富的数据，利用最新的数据处理工具来生成有关 STEM 教学的新见解。他们致力于最大限度地发挥潜力，同时降低自动化数据收集和分析的风险，他们将：(1) 收集并集成不同来源的数据，包括来自两台不同计算机的八个不同的两周活动的日志文件、视频和书面工件支持的学习环境（一种用于中学数学，一种用于高中科学）； (2) 将日志文件数据的分析与综合数据集的分析进行比较，以了解使用日志文件数据评估学生学习和熟练程度的可能性和局限性。合作者期望他们的研究结果将为适用于广泛学科和环境的理论和实践建议提供信息。