将人文学科融入数据科学教育

Q3 Social Sciences
Eric A. Vance, David R. Glimp, Nathan D. Pieplow, Jane M. Garrity, B. Melbourne
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引用次数: 3

摘要

尽管越来越多的人呼吁培养数据科学学生的道德意识,并将以人为中心的方法扩展到数据科学教育中,但该领域的入门课程仍然主要是技术性的。一个新的跨学科数据科学项目旨在从数据科学课程的一开始就融合STEM和人文学科的观点。现有文献表明,人文学科的融合可以使STEM课程对更广泛的学生更具吸引力,包括女性和有色人种学生,并增强学生对基本概念和基本推理技能的学习,比如统称为数据敏感性的那些。培养学生的数据敏锐度需要对通过计算方法和统计分析产生的知识和见解如何与其他认识方式联系起来有一个更包容的视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
INTEGRATING THE HUMANITIES INTO DATA SCIENCE EDUCATION
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.
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来源期刊
Statistics Education Research Journal
Statistics Education Research Journal Social Sciences-Education
CiteScore
1.30
自引率
0.00%
发文量
46
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.
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