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

数据科学深度学习 (DLDS)：研究工程专业人员的真实经验，为未来的劳动力做好准备

基本信息

批准号：
1712129
负责人：
Aditya Johri
金额：
$ 30万
依托单位：
George Mason University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-15 至 2021-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1712129&HistoricalAwards=false
关键词：
Deeper Learning Data Science DLDS

项目摘要

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.

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此类数据来解决缺乏受过大规模数字数据处理、分析和使用培训的专家的问题。如果正确分析和利用，数字数据可以带来有用的见解，帮助人们、公司和政府机构开展从教育和医疗保健到无人机和飞机设计的一系列工作。从这项研究中获得的知识将用于设计课程材料，以培训未来的工程师处理数据。特别是，将在课堂上生成和测试如何使用数据产生影响的案例研究。这些案例研究可供其他想要提供类似培训的人使用。这项工作将涵盖广泛的主题，研究将从不同工程师的角度进行学习，特别强调向那些在工程领域通常代表性不足的工程师学习。数据科学被认为是一个很难找到训练有素的从业者的关键领域。该项目的智力价值在于研究工程专业人员如何处理数据密集型项目，以了解其工作所需的知识、技能和能力。将开展案例研究来培训工程本科生。将利用“专业愿景”和“纪律感知”两种相互关联的理论方法来概念化本研究，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，他们需要学习哪些新知识以及他们如何获得这些知识？将进行由访谈和调查组成的混合方法实地研究。 30 名专业人士将在两年内接受两次访谈（60 次访谈），并对 250 名参与者进行调查。该研究有望通过为行业所需的数据科学技能提供全面指导，并加深对专业工程工作如何变化的理解，从而促进学生的学习。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Insights for Curriculum Development: Identifying Emerging Data Science Topics through Analysis of Q&A Communities

DOI：
10.1145/3328778.3366817
发表时间：
2020-01-01
期刊：
SIGCSE 2020: PROCEEDINGS OF THE 51ST ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION
影响因子：
0
作者：
Karbasian, Habib;Johri, Aditya
通讯作者：
Johri, Aditya

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