面向微观学习的宏观数据:开发FUN!学习的自动评估工具

Taylor Martin, S. Brasiel, Soojeong Jeong, Kevin Close, Kevin Lawanto, Phil Janisciewcz
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引用次数: 3

摘要

数字学习环境对学生在校内外的参与变得越来越普遍。由于现在可以从这些环境中获得大量数据,研究人员需要工具来处理、管理和分析数据。目前许多教育研究者使用的方法效率低下;然而,如果没有数据科学经验,就无法使用其他行业使用的工具。在本文中,我们将分享我们创建的一个工具,称为功能理解导航器!(好玩!工具)。我们在不同的研究项目中使用了这个工具,这使我们有机会(1)从头到尾组织我们的工作流程,(2)记录我们所有分析的日志数据,(3)提供一个平台,通过GitHub与他人分享我们的分析。本文扩展和改进了教育数据挖掘和学习分析方面的现有工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Macro Data for Micro Learning: Developing the FUN! Tool for Automated Assessment of Learning
Digital learning environments are becoming more common for students to engage in during and outside of school. With the immense amount of data now available from these environments, researchers need tools to process, manage, and analyze the data. Current methods used by many education researchers are inefficient; however, without data science experience tools used in other professions are not accessible. In this paper, we share about a tool we created called the Functional Understanding Navigator! (FUN! Tool). We have used this tool for different research projects which has allowed us the opportunity to (1) organize our workflow process from start to finish, (2) record log data of all of our analyses, and (3) provide a platform to share our analyses with others through GitHub. This paper extends and improves existing work in educational data mining and learning analytics.
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