{"title":"BIG-DATA LITERACY AS A NEW VOCATION FOR STATISTICAL LITERACY","authors":"Karen François, C. Monteiro, P. Allo","doi":"10.52041/SERJ.V19I1.130","DOIUrl":null,"url":null,"abstract":"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. \nFirst published February 2020 at Statistics Education Research Journal Archives","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics Education Research Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52041/SERJ.V19I1.130","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 14
Abstract
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
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
