Eric A. Vance, David R. Glimp, Nathan D. Pieplow, Jane M. Garrity, B. Melbourne
{"title":"INTEGRATING THE HUMANITIES INTO DATA SCIENCE EDUCATION","authors":"Eric A. Vance, David R. Glimp, Nathan D. Pieplow, Jane M. Garrity, B. Melbourne","doi":"10.52041/serj.v21i2.42","DOIUrl":null,"url":null,"abstract":"Despite growing calls to develop data science students’ ethical awareness and expand human-centered approaches to data science education, introductory courses in the field remain largely technical. A new interdisciplinary data science program aims to merge STEM and humanities perspectives starting at the very beginning of the data science curriculum. Existing literature suggests that humanities integration can make STEM courses more appealing to a wider range of students, including women and students of color, and enhance student learning of essential concepts and foundational reasoning skills, such as those collectively known as data acumen. Cultivating students’ data acumen requires a more inclusive vision of how the knowledge and insights generated through computational methods and statistical analysis relates to other ways of knowing.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics Education Research Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52041/serj.v21i2.42","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: 3
Abstract
Despite growing calls to develop data science students’ ethical awareness and expand human-centered approaches to data science education, introductory courses in the field remain largely technical. A new interdisciplinary data science program aims to merge STEM and humanities perspectives starting at the very beginning of the data science curriculum. Existing literature suggests that humanities integration can make STEM courses more appealing to a wider range of students, including women and students of color, and enhance student learning of essential concepts and foundational reasoning skills, such as those collectively known as data acumen. Cultivating students’ data acumen requires a more inclusive vision of how the knowledge and insights generated through computational methods and statistical analysis relates to other ways of knowing.
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.