基于社区的教育数据库和分析工具

K. Koedinger, Ran Liu, John C. Stamper, Candace Thille, P. Pavlik
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引用次数: 3

摘要

本次研讨会将探讨基于社区的教育数据存储库和分析工具,用于连接研究人员并减少数据共享的障碍。该领域的领先创新者,以及与会者,将识别并报告实现统一存储库目标的瓶颈。我们将讨论这些以及可能的解决方案。我们将展示LearnSphere,这是一个NSF资助的系统,它支持协作和共享各种教育数据、学习分析方法和可视化,同时保持机密性。然后,我们将有实践会议,与会者有机会将现有的学习分析工作流应用到存储库中他们选择的教育数据集(使用简单的拖放界面),添加他们自己的学习分析工作流(需要非常基本的编码经验),或两者兼而有之。然后,领导和与会者将共同讨论这些解决方案的独特优势以及局限性。我们的目标是创建构建模块,使研究人员能够将他们的数据和分析方法与他人集成,以推进学习科学的未来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Community based educational data repositories and analysis tools
This workshop will explore community based repositories for educational data and analytic tools that are used to connect researchers and reduce the barriers to data sharing. Leading innovators in the field, as well as attendees, will identify and report on bottlenecks that remain toward our goal of a unified repository. We will discuss these as well as possible solutions. We will present LearnSphere, an NSF funded system that supports collaborating on and sharing a wide variety of educational data, learning analytics methods, and visualizations while maintaining confidentiality. We will then have hands-on sessions in which attendees have the opportunity to apply existing learning analytics workflows to their choice of educational datasets in the repository (using a simple drag-and-drop interface), add their own learning analytics workflows (requires very basic coding experience), or both. Leaders and attendees will then jointly discuss the unique benefits as well as the limitations of these solutions. Our goal is to create building blocks to allow researchers to integrate their data and analysis methods with others, in order to advance the future of learning science.
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