2024 ASEE Annual Conference & Exposition

This paper presents the findings from a qualitative study that investigates how mechanical and aerospace engineering (MAE) undergraduate students define and develop data proficiency. For the purposes of this study, data is defined as an object, variable, or piece of information that has the perceived capacity to be collected, stored, and identifiable. Correspondingly, data skills refer to abilities to interpret data, make connections, and turn data into meanings to solve a problem effectively. To have data proficiency, students must have 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. They will continue to transform the engineering workplace and are instrumental in the ongoing radical shift of jobs enabled by automation. Through the lens of the How People Learn theoretical framework, we analyze the qualitative data to find insights into the following research questions:
1. How do MAE undergraduate students conceptualize data proficiency?
2. How do MAE undergraduate students identify the opportunities to develop data proficiency in their academic trajectory?
We collected 27 collective qualitative interviews comprising 3 first years, 5 sophomores, 10 juniors, and 8 seniors at a research institution in the southeastern United States. Leveraging How People Learn framework, data were analyzed through thematic analysis methods using a post-positivist approach, given the bounded context of this study. Findings indicate that MAE students recognize data proficiency as a vital skill for their future careers and appreciate its importance in making informed engineering decisions. The research also shows that, despite data proficiency often being a "hidden competency," MAE students instinctively find many ways to develop data skills. These findings may inform engineering educators on how to meet their students where they are, identify misconceptions about data and data proficiency, and educate a data-literate future engineering workforce.