Convergence HDR: Social Science Insights for 21st Century Data Science Education (SSI)

将举办两个研讨会，以开发创新的方法，将有关数据科学实践的社会科学发现和教育研究的见解纳入数据科学课程。该系列研讨会将发展21世纪数据科学教育的要素，以在学科研究环境中进行数据科学的实践为基础。研讨会参与者将与实践数据科学家、教育工作者和社会科学家一起组成一个实践社区。该项目通过整合社会科学研究和教育研究的见解来促进融合，解决如何教授数据科学和开发适当的课程材料的问题。第一次研讨会计划为期1.5天，约有50名参与者，并将努力从社会科学研究和教育研究中找出数据科学实践的关键见解，以服务于他们的数据科学课程。它将借鉴一系列社会科学学科（如科学和技术研究、跨学科合作的人种学观察、计算机支持的合作工作、团队的包容性和多样性、组织/管理理论、设计中的系统思维和人/技术界面）的研究成果，以及与学科数据科学实践者和教育者一起进行的教育研究和设计。在小组讨论和工作会议中，研讨会将吸引参与者构建与数据科学相关的关键主题，并制定针对这些主题的课程设计方法。本次研讨会的成果将是一份公开的白皮书，确定下一阶段工作的核心见解。在第一次和第二次讲习班之间的间隔期间，将记录和改进第一次讲习班的观察和见解，供第二次讲习班使用。为了促进交叉应用，研讨会组织者还将与选定的行业从业者联系，以评估将数据科学概念转化为现实世界应用的重要性。第二次工作坊为期1天，约有30人参加。它将概述和构建可实现的方法，将关键见解转化为数据科学课程。全天的主题会议将以第一次研讨会中提出的关键见解分类为基础，讨论围绕实际实施和新课程形式原型的实质性问题。本次研讨会的成果将是一套充实的课程内容（如练习、课程模块等）和公开传播的最佳实践指南。这些研讨会将聚焦数据科学的实践，作为数据科学教育者和社会科学家参与课程设计的关键领域。该项目通过关注实践者和实践来重新定位跨学科数据科学教育议程。这些研讨会将为数据科学教育开发可实施的、公开的课程元素和最佳实践，以数据科学的协作为导向，关注多样性、包容性、社会和伦理背景等重要问题。

A pair of workshops will be conducted to develop innovative ways of incorporating social science findings about the practice of data science and insights from educational research into data science curricula. The workshop series will develop elements of 21st-century data science education anchored in the doing of data science in disciplinary research settings. Workshop participants will form a community of practice with practicing data scientists, educators, and social scientists working together. This project promotes Convergence by integrating insights from social science research and education research in the context of addressing the problem of how to teach data science and develop the appropriate curricular materials. The first workshop is planned for 1.5 days with about 50 participants, and will strive to identify key insights from social science research and education research into the practice of data science, in the service of their translation into data science curriculum. It will draw on findings from a range of social science disciplines (such as science and technology studies, ethnographic observation of transdisciplinary collaboration, computer supported cooperative work, work on inclusion and diversity in teams, organizational / management theory, systems thinking in design, and the human/technology interface) and educational research and design together with disciplinary data science practitioners and educators. In panels and working sessions, the workshop will draw in participants to frame key themes associated with the doing of data science and develop ways of targeting these to curriculum design. The product from this workshop will be a publicly available white paper identifying core insights for the next stage of work.In the intervening period between the first and second workshops, the observations and insights from the first workshop will be recorded and refined for use by the second workshop. In the interest of cross-fertilization, the workshop organizers will also make contact with select industry practitioners to assess the importance of the identified concepts for data science translated to real world application. The second workshop will be for 1 day with about 30 participants. It will outline and construct implementable approaches for translating the key insights into data science curricula. Thematic sessions through the day will build on the taxonomy of key insights developed in the first workshop, and address substantive issues around practical implementation, and prototyping of new curricular forms. The products from this workshop will be a fleshed-out set of curricular elements (such as exercises, course modules, etc.) and best practices guidelines to be publicly disseminated.These workshops will spotlight the doing of data science as a key arena for collaboratively engaging data science educators and social scientists in curriculum design. This project reorients the transdisciplinary data science education agenda by focusing on practitioners and practice. The workshops will develop implementable, publicly available curricular elements and best practices for data science education oriented to the collaborative doing of data science, with attention to important issues such as diversity, inclusion, and societal and ethical context.