NSF Award
2245022
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. <br/><br/>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.<br/><br/>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.
