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研究不同领域的工程师目前如何处理这类数据，来解决缺乏在处理、分析和使用大规模数字数据方面受过培训的专家的问题。如果分析和利用得当，数字数据可以带来有用的见解，可以帮助人们、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工作中发挥作用。从这项研究中获得的知识将用于设计课程材料，以培养未来的工程师与数据的工作。特别是，将在类中生成和测试如何使用数据产生影响的案例研究。这些案例研究将供其他希望提供类似培训的人使用。广泛的主题将包括在这项工作中，研究研究将从不同范围的工程师的角度学习，特别强调向那些在工程领域代表性不足的人学习。数据科学被认为是一个很难找到训练有素的从业人员的关键领域。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中工作，以便了解他们工作所需的知识、技能和能力。案例研究将用于培养工程专业的本科生。两个相互关联的理论方法“专业视野”和“纪律感知”将利用以下研究问题来概念化本研究：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)他们在工作中使用了哪些技术、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，他们必须学习哪些新知识，他们如何获得这些知识？将进行包括访谈和调查在内的混合方法实地研究。30位专业人士将在两年的时间内接受两次访谈（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，为行业所需的数据科学技能提供全面指导，并促进对专业工程工作变化的理解。

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.