Deeper Learning of Data Science (DLDS): Studying Real-world Experiences of Engineering Professionals to Prepare the Future Workforce

乔治梅森大学将通过对不同领域的工程师目前如何处理这类数据进行研究，解决缺乏受过处理、分析和使用大规模数字数据培训的专家的问题。如果分析和利用得当，数字数据可以产生有用的见解，帮助人们、公司和政府机构在从教育和医疗保健到无人机和飞机设计的一系列努力中赋权。从这项研究中获得的知识将被用于设计课程材料，以培训未来的工程师使用数据。特别是，将在一个班级内生成和测试如何将数据用于影响的案例研究。这些案例研究将可供其他希望提供类似培训的人使用。这项工作将包括广泛的主题，研究性研究将从不同类型的工程师的角度进行学习，特别强调向那些通常在工程学中代表性不足的人学习。数据科学已被确定为一个关键领域，在这个领域很难找到训练有素的从业人员。这个项目的智力价值在于它研究了工程专业人员如何在数据密集型项目上工作，以便了解他们的工作所需的知识、技能和能力。将开发案例研究，以培养工科本科生。本研究将利用两种相互关联的理论方法--“专业视野”和“有纪律的认知”来概念化这项研究，并提出以下研究问题：1)数据专业人员在进行数据密集型工作时面临哪些背景挑战，以及他们如何克服这些挑战；2)他们在工作中利用了哪些技术、专业知识和特定领域的知识；3)他们从以前的经验中转移了哪些知识，他们学到了哪些必要的新知识，以及他们是如何获得这些知识的？将进行一项由访谈和调查组成的混合方法实地研究。将在两年内对30名专业人员进行两次访谈(60次访谈)，并对250名参与者进行调查。这项研究有望促进学生的学习，提供行业所需的数据科学技能的全面指导，并增进对专业工程工作如何变化的理解。

George Mason University will address the lack of experts trained in the processing, analysis and use of large-scale digital data by conducting a study of how engineers in different fields currently work with this type of data. If properly analyzed and utilized, digital data can lead to useful insights that can help empower people, companies, and government agencies across a range of efforts from education and healthcare, to the design of drones and aircrafts. The knowledge gained from this study will be used to design curricular materials to train future engineers to work with data. In particular, case studies of how data can be used for impact will be generated and tested within a class. These case studies will be available for use by others who want to provide similar training. A broad range of topics will be included in this work and the research study will learn from the perspectives of a diverse range of engineers, with particular emphasis on learning from those who are typically underrepresented in engineering. Data Science has been identified as a critical domain in which trained practitioners are hard to find. The intellectual merit of this project is its study of how engineering professional work on data-intensive projects in order to understand the knowledge, skills, and competencies required for their work. Case studies will be developed to train undergraduate engineering students. Two interrelated theoretical approaches 'Professional Vision' and 'Disciplined Perception' will be leveraged to conceptualize this study with the following research questions: 1) What contextual challenges do data professionals face while conducting data-intensive work and how do they overcome them; 2) What techniques, professional expertise, and domain-specific knowledge do they draw on for their work; 3) What knowledge do they transfer from prior experiences and what new knowledge do they learn of necessity and how do they acquire it? A mixed-methods field study comprised of interviews and surveys will be conducted. Thirty professionals will be interviewed twice over a period of two years (60 interviews) and a survey of 250 participants will be conducted. The research has promise for advancing student learning, by providing overall guidance on data science skills desired by the industry, and by advancing understanding of how professional engineering work has changed.