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

将举办两期讲习班，以开发将社会科学关于数据科学实践的研究成果和教育研究的见解纳入数据科学课程的创新方法。该系列研讨会将开发21世纪数据科学教育的要素，立足于在学科研究环境中进行数据科学。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实践社区。该项目通过将社会科学研究和教育研究的见解结合到如何教授数据科学和开发适当的课程材料的背景下来促进融合。第一期讲习班计划为期1.5天，约50人参加，将努力确定社会科学研究和教育研究对数据科学实践的关键见解，以便将其转化为数据科学课程。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工作、组织/管理理论、设计中的系统思维以及人/技术接口)以及教育研究和设计的成果，以及学科数据科学从业人员和教育工作者。在小组讨论和工作会议中，研讨会将吸引参与者来框定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。这次研讨会的成果将是一份公开提供的白皮书，为下一阶段的工作确定核心见解。在第一次和第二次研讨会之间的间歇期，第一次研讨会的观察和见解将被记录下来，并加以完善，供第二次研讨会使用。为了促进交流，研讨会组织者还将与选定的行业从业者联系，以评估已确定的数据科学概念转化为现实世界应用的重要性。第二期工作坊为期一天，约30人参加。它将概述并构建可实施的方法，将关键见解转化为数据科学课程。全天的专题会议将建立在第一期讲习班提出的关键见解的分类基础上，并处理有关实际实施和新课程形式原型的实质性问题。这次研讨会的产品将是一套充实的课程要素(如练习、课程模块等)。这些讲习班将重点介绍数据科学的工作，作为与数据科学教育工作者和社会科学家合作设计课程的关键舞台。该项目通过关注实践者和实践，重新定位了跨学科数据科学教育议程。讲习班将为数据科学教育制定可实施的、可公开获得的课程内容和最佳做法，以开展数据科学的协作工作，同时关注诸如多样性、包容性以及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.