Keywords: Data Science Education, Student Difficulties, Data Science Concepts, Education Research
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 talk will discuss early work in data science education research. We will discuss grant work that 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.
In this talk I will detail the experimental process and early findings from our research. Specifically, I will discuss collecting data and documentation of conceptual misunderstandings and difficulties in data science. The early data aims to (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 gathered 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. This talk will report on the preliminary findings and data collected during Spring 2019.