The data science education dilemma
The data science education dilemma
复制标题
数据科学教育的困境
DOI：
10.52041/srap.12105
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
W. Finzer
中科院分区：
文献类型：
--
作者：
W. Finzer
对熟练处理数据的人的需求正在迅速而巨大地增长，但美国的K-12教育并没有提供有意义的学习经验，旨在培养对数据科学概念的理解或熟练掌握数据科学技能。数据科学本质上是跨学科的，因此将其与现有的内容领域集成是有意义的，但困难重重。考虑到数据科学所涉及的工作及其背后的思维习惯，我们可以将数据科学与数学和科学相结合。Data Games项目中当前活动开发的例子揭示了基于技术和数据驱动的情况。该项目正在进行的关于学习者组织数据的概念以及与数据科学教育的相关性的研究进行了解释。
The need for people fluent in working with data is growing rapidly and enormously, but U.S. K–12 education does not provide meaningful learning experiences designed to develop understanding of data science concepts or a fluency with data science skills. Data science is inherently inter- disciplinary, so it makes sense to integrate it with existing content areas, but difficulties abound. Consideration of the work involved in doing data science and the habits of mind that lie behind it leads to a way of thinking about integrating data science with mathematics and science. Examples drawn from current activity development in the Data Games project shed some light on what technology-based, data-driven might be like. The project’s ongoing research on learners’ conceptions of organizing data and the relevance to data science education is explained.