Research Initiation: Investigating the Connection Among Undergraduate Engineering Students Data Proficiency, Motivation, and Engineering Identity

数据和数据分析在所有工程领域越来越普遍。对于工程专业的学生来说，保持和提高他们的数据技能，学习先进的技术，在今天和未来的就业市场上具有竞争力，这是至关重要的。工程专业的学生和从业者几乎在所有的专业活动中都需要生成、解释和操作数据。数据技能被定义为解释数据、建立联系并将数据转化为意义以解决问题的能力，范围从重构数据的基本方法到机器学习和人工智能等高级技术。在培养数据熟练度的过程中，学生将建立情境化、解释和操作数据的能力，以满足利益相关者的需求。数据、数据技能和数据熟练度对工程专业的学生来说至关重要，因为数据支持技术的需求量很大，因为它们将继续改变工程工作场所，并有助于正在进行的被自动化取代和增强的工作的根本性转变。然而，迄今为止，建立数据熟练度的经验与工程身份和动机之间的联系尚未得到调查和建立。本研究启动项目的总体目标是采用混合方法研究方法来发现数据熟练程度的发展如何与整个本科工程课程的动机和工程身份联系在一起。从动机理论和工程认同理论来看，指导本项目的研究问题是：(1)数据熟练度的发展如何支持工科本科学生的专业形成？(2)学生动机和个人认同如何影响农村赠地大学工科本科生数据熟练度和工程认同的发展？为了回答这些问题，我们提出了两阶段混合方法的研究设计。定性阶段将在两年的资助期内对学生群体进行两次访谈，以了解数据技能和数据熟练程度的发展是如何建立的，以及如何与工程身份和动机相互作用。这些发现将影响现有调查工具的调整，以更广泛地理解数据熟练程度、工程身份和动机之间的潜在联系。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Data and data analysis are increasingly pervasive in all engineering fields. It is critical for engineering students to retain and improve their data skills and to learn advanced techniques to be competent in the job market today and in the future. Engineering students and practitioners need to generate, interpret, and manipulate data in nearly all their professional activities. Data skills are defined as the ability to interpret data, make connections, and turn data into meanings to solve a problem ranging from foundational methods for refactoring data to advanced techniques such as machine learning and artificial intelligence. In developing data proficiency, students build the ability to contextualize, interpret, and manipulate data to meet stakeholder needs. Data, data skills, and data proficiency are critical for engineering students because data-enabled technologies are in high demand as they will continue to transform the engineering workplace and are instrumental in the ongoing radical shift of jobs replaced and augmented by automation. However, to date, the connection between the experiences building data proficiency and engineering identity and motivation has not been investigated and established. The overarching goal of this research initiation project is to employ a mixed-methods research approach to discovering how the development of data proficiency is linked with motivation and engineering identity throughout the undergraduate engineering curriculum. The research questions that guide this project, viewed through motivation theory and engineering identity theory are (1) How does the development of data proficiency support the professional formation of undergraduate engineering students? (2) How do facets of student motivation and personal identities influence the development of data proficiency and engineering identity for undergraduate engineering students at a rural land grant university? To answer these questions, we propose a two-phase mixed methods research design. The qualitative phase will interview student cohorts twice over the two years of the grant to understand how engagement with data skills and the development of data proficiency is built and interacts with engineering identity and motivation. These findings will influence the adaption of existing survey instruments to understand potential connections between data proficiency, engineering identity, and motivation more broadly.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.