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

Valparaiso University, Valparaiso IN

Investigators

Abstract

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.