Can You Teach Me To Machine Learn? An Exploration of Pedagogical Content Knowledge for Teaching Machine Learning to Non-Majors

Machine learning (ML) has become an important topic for students across disciplines to understand because of its useful applications and its societal impacts. At the same time, there is little existing work on ML education, particularly about teaching ML to non-majors. This paper presents an exploration of the pedagogical content knowledge (PCK) for teaching ML to non-majors. Through ten interviews with instructors of ML courses for non-majors, we inquired about student preconceptions as well as what students find easy or difficult about learning ML. We identified PCK in the form of three preconceptions and five barriers faced by students, and six pedagogical tactics adopted by instructors. The preconceptions were found to concern themselves more with ML's reputation rather than its inner workings. Student barriers included underestimating human decision in ML and conflating human thinking with computer processing. Pedagogical tactics for teaching ML included strategically choosing datasets, walking through problems by hand, and customizing to the domain(s) of students. As we consider the lessons from these findings, we hope that this will serve as a first step toward improving the teaching of ML to non-majors.