学校里的数据科学项目:介于统计学和认知编程之间

Q3 Social Sciences

Statistics Education Research Journal Pub Date : 2022-07-04 DOI:10.52041/serj.v21i2.46

Susanne Podworny, Sven Hüsing, Carsten Schulte

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引用次数: 2

摘要

数据科学的各个方面在许多情况下围绕着我们，例如气候变化、空气污染和其他环境问题。为了为初中生打开“数据科学黑匣子”，我们开发了一个数据科学项目，重点分析自己收集的环境数据。我们将该项目嵌入计算机科学教育中，使我们能够在Jupyter Notebooks和编程语言Python中使用新的基于知识的编程方法进行数据分析。在本文中，我们评估了这个项目的第二个周期，它发生在九年级的计算机科学课上。特别是，我们介绍了学生们如何使用Jupyter Notebooks的专业工具进行统计调查，以及他们获得了哪些见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A PLACE FOR A DATA SCIENCE PROJECT IN SCHOOL: BETWEEN STATISTICS AND EPISTEMIC PROGRAMMING

Aspects of data science surround us in many contexts, for example regarding climate change, air pollution, and other environmental issues. To open the “data-science-black-box” for lower secondary school students we developed a data science project focussing on the analysis of self-collected environmental data. We embed this project in computer science education, which enables us to use a new knowledge-based programming approach for the data analysis within Jupyter Notebooks and the programming language Python. In this paper, we evaluate the second cycle of this project which took place in a ninth-grade computer science class. In particular, we present how the students coped with the professional tool of Jupyter Notebooks for doing statistical investigations and which insights they gained.

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来源期刊

Statistics Education Research Journal Social Sciences-Education

CiteScore

1.30

自引率

0.00%

发文量

期刊介绍： SERJ is a peer-reviewed electronic journal of the International Association for Statistical Education (IASE) and the International Statistical Institute (ISI). SERJ is published twice a year and is free. SERJ aims to advance research-based knowledge that can help to improve the teaching, learning, and understanding of statistics or probability at all educational levels and in both formal (classroom-based) and informal (out-of-classroom) contexts. Such research may examine, for example, cognitive, motivational, attitudinal, curricular, teaching-related, technology-related, organizational, or societal factors and processes that are related to the development and understanding of stochastic knowledge. In addition, research may focus on how people use or apply statistical and probabilistic information and ideas, broadly viewed. The Journal encourages the submission of quality papers related to the above goals, such as reports of original research (both quantitative and qualitative), integrative and critical reviews of research literature, analyses of research-based theoretical and methodological models, and other types of papers described in full in the Guidelines for Authors. All papers are reviewed internally by an Associate Editor or Editor, and are blind-reviewed by at least two external referees. Contributions in English are recommended. Contributions in French and Spanish will also be considered. A submitted paper must not have been published before or be under consideration for publication elsewhere.