Exploring Self-Efficacy in Data Science

Abstract

Data science is often heralded as a key learning goal for students in STEM classrooms. There are also myriad efforts to integrate data science into these classrooms, and many dedicated research efforts for identifying the best ways to do so. However, the problem is that there is little agreement on how to introduce data science to students, whether it be through computer science courses where students can learn programming, through STEM courses where students can learn disciplinary knowledge, or through newly designed data science centric courses. Furthermore, best practices for teaching data science require an understanding of what data science is from students' perspectives, and how they experience it. This poster explores this problem by showcasing an interview study of an undergraduate course offered at Michigan State University, which focuses on computational modeling and data analysis. Students in this course learn data science via problem-based group work and apply it to several disciplinary contexts. The interview study examines how students perceived what they learned, and how their self-efficacy developed over the course of the semester. In effect, we demonstrate a course where students are learning data science, identify the key features of the course that students perceive, and build an understanding of data science self-efficacy, which can be used to help design positive, effective experiences in data science courses.