大数据素养作为统计素养的新职业

Q3 Social Sciences

Statistics Education Research Journal Pub Date : 2020-02-29 DOI:10.52041/SERJ.V19I1.130

Karen François, C. Monteiro, P. Allo

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引用次数: 14

摘要

在当代社会中，大量的数据是通过各种方式不断产生的，它们被称为大数据集。大数据的潜力和局限性需要统计学家和统计消费者理解，因此，发展大数据素养以支持建设性、关注性和反思性公民的需求是一项挑战。然而，统计素养概念的发展反映了当前大数据的纯技术特征和社会政治特征之间的差距。在本文中，我们回顾了统计素养概念的最新历史，并强调需要整合与大数据相关的数据科学的新挑战和关键问题，包括伦理学、认识论、数学论证和数学清洗。2020年2月首次发表于《统计教育研究期刊档案》

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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BIG-DATA LITERACY AS A NEW VOCATION FOR STATISTICAL LITERACY

In the contemporary society a massive amount of data is generated continuously by various means, and they are called Big-Data sets. Big Data has potential and limits which need to be understood by statisticians and statistics consumers, therefore it is a challenge to develop Big-Data Literacy to support the needs of constructive, concerned, and reflective citizens. However, the development of the concept of statistical literacy mirrors the current gap between purely technical and socio-political characterizations of Big Data. In this paper, we review the recent history of the concept of statistical literacy and highlight the need to integrate the new challenges and critical issues from data science associated with Big Data, including ethics, epistemology, mathematical justification, and math washing. First published February 2020 at Statistics Education Research Journal Archives

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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.