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

这个教育和学习研究（真实的）项目源于2014年10月关于数据密集型研究的想法实验室，以改善教学。这项工作的目的是（1）将来自不同学科的研究人员聚集在一起，以促进新的，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，为中期改善STEM教学和学习环境创造可操作的知识;（2）从长远来看，彻底改变学习。在这个项目中，来自亚利桑那州州立大学和西北大学的研究人员将合作，以提高对学习影响的理解，并改善高中和中学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许多不同的数据流，这些数据流可以在技术丰富的教室中收集，以（1）确定影响学习的课堂因素，（2）探索如何使用这些数据自动跟踪学生的理解和能力随时间的发展。有两股力量准备改变学习研究。首先，越来越多的学生作业是在计算机和网络上进行的，产生了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实现了智能和响应式系统，为个人学习者提供个性化的学习体验。PI旨在收集远远超出典型计算机数据捕获的高度丰富的数据，利用最新的数据处理工具来生成有关STEM教学和学习的新见解。为了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）收集和整合来自两个不同计算机支持学习环境的八个不同的为期两周的法规的各种数据源，包括日志文件，视频和书面工件（一个用于中学数学，一个用于高中科学）;和（2）比较日志文件数据的分析与综合数据集的分析，以了解使用日志的可能性和局限性。档案数据，以评估学生的学习和熟练程度。合作者希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。

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.