TRIPODS+X: EDU: Collaborative Research: Investigations of Student Difficulties in Data Science Instruction

网络浏览历史、在线报纸、流媒体音乐和股票价格都表明，我们生活在一个数据时代。在许多领域中，从数据中提取意义是理解信息流所必需的。这种需求推动了旨在服务于下一代政策制定者、数据科学研究人员和全球公民的数据科学教育的快速增长。最初，教学实践来自于数据科学的母学科(例如，计算机科学和数学)。该项目开始了将数据科学教育作为自己的研究领域进行调查的过程。该项目旨在确定学生第一次进入数据科学课堂时可能存在的偏见，以及相关课程中的哪些课程正在塑造他们的偏见。该项目进行了一项混合方法的教育调查，以收集数据科学中概念误解和困难的数据和记录。这项调查将(1)确定目前教授美国国家科学、工程和医学研究院(NASEM)报告：本科生的数据科学：机会和选择中确定的关键主题的不同学科的班级；(2)与这些课程的教师合作，收集学生围绕这些主题思考(特别是误解)的证据；以及(3)调查早期职业数据科学从业者，以评估那些持续存在的误解。在这次教育调查中，我们将收集学生的工作，因为这些学生首先接触到数据科学的概念以及那些课程中使用的教材。其他研究方法包括学生访谈和调查。第(3)项，直接与数据科学从业者合作，将根据当前的劳动力需求，帮助确定教师可能遗漏的尖端技术主题和疏忽。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Web-browsing histories, online newspapers, streaming music, and stock prices all show that we live in an age of data. Extracting meaning from data is necessary in many fields to comprehend the information flow. This need has fueled rapid growth in data science education aiming to serve the next generation of policy makers, data science researchers, and global citizens. Initially, teaching practices have been drawn from data science's parent disciplines (e.g., computer science and mathematics). This project begins the process of investigating data science education as its own field of research. It aims to identify preconceptions students may have when they first enter a data science classroom, and what other courses from related programs are shaping their preconceptions.This project conducts a mixed-method educational investigation to collect data and documentation of conceptual misunderstandings and difficulties in data science. This investigation will (1) identify classes in a variety of disciplines currently teaching the critical topics identified in the National Academy of Sciences, Engineering, and Medicine (NASEM) Report: Data Science for Undergraduates: Opportunities and Options; (2) work with instructors of those courses to gather evidence of student thinking (especially misconceptions) surrounding those topics; and (3) survey early career data science practitioners to assess those misconceptions that persist to employment. During this educational investigation, we will gather student work as those students are first engaging with data science concepts as well as the teaching materials used in those courses. Additional research methodologies include student interviews and surveys. Item (3), working directly with data science practitioners, will assist in identifying cutting-edge technical topics and oversights by instructors that might be otherwise missed, based on current workforce demands.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.