Bridging sustainability science, earth science, and data science through interdisciplinary education

被引:0
作者
Deana Pennington
Imme Ebert-Uphoff
Natalie Freed
Jo Martin
Suzanne A. Pierce
机构
[1] University of Texas at El Paso,Department of Geological Sciences
[2] Cooperative Institute for Research in the Atmosphere,Department of Curriculum and Instruction
[3] Colorado State University,Electrical and Computer Engineering
[4] University of Texas at Austin,undefined
[5] Mathematics and Statistics,undefined
[6] University of Vermont,undefined
[7] Texas Advanced Computing Center,undefined
[8] University of Texas at Austin,undefined
[9] Colorado State University,undefined
来源
Sustainability Science | 2020年 / 15卷
关键词
Education; Interdisciplinary studies; Competencies; Data science applications;
D O I
暂无
中图分类号
学科分类号
摘要
Given the rapid emergence of data science techniques in the sustainability sciences and the societal importance of many of these applications, there is an urgent need to prepare future scientists to be knowledgeable in both their chosen science domain and in data science. This article provides an overview of required competencies, educational programs and courses that are beginning to emerge, the challenges these pioneering programs face, and lessons learned by participating instructors, in the broader context of sustainability science competencies. In addition to data science competencies, competencies collaborating across disciplines are essential to enable sustainability scientists to work with data scientists. Programs and courses that target both sets of competencies—data science and interdisciplinary collaboration—will improve our workforce capacity to apply innovative new approaches to yield solutions to complex sustainability problems. Yet developing these competencies is difficult and most instructors are choosing instructional approaches through intuition or trial and error. Research is needed to develop effective pedagogies for these specific competencies.
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页码:647 / 661
页数:14
相关论文
共 257 条
[1]  
Barile S(2018)People, technology, and governance for sustainability: the contribution of systems and cyber-systemic thinking Sustain Sci 2018 1-12
[2]  
Orecchini F(2018)Realizing the potential of data science Commun ACM 61 67-72
[3]  
Saviano M(2017)Science and data science Proc Natl Acad Sci 114 8689-8692
[4]  
Farioli F(2016)A pedagogical model for team-based, problem-focused interdisciplinary doctoral education BioScience 2016 biw042-67
[5]  
Berman F(2013)Research collaboration in universities and academic entrepreneurship: the-state-of-the-art J Technol Transfer 38 1-1309
[6]  
Stodden V(2018)Investigating the role of smartness for sustain- ability: insights from the smart grid domain Sustain Sci 13 1299-72
[7]  
Szalay AS(2018)On the role of statistics in the era of big data: A computer science perspective Stat Prob Lett 136 68-21
[8]  
Rutenbar R(2006)Multidisciplinarity, interdisciplinarity and transdisciplinarity in health research, services, education and policy: 1. Definitions, objectives, and evidence of effectiveness Clin Investig Med 29 351-64
[9]  
Hailpern B(1990)Grounded theory research: procedures, canons, and evaluative criteria Qual Sociol 13 3-163
[10]  
Christensen H(2019)Upscaling urban data science for global climate solutions Glob Sustain 2 e2-78