Motivating Data Science Students to Participate and Learn

Abstract

Data science education is increasingly involving human subjects and societal issues such as privacy, ethics, and fairness. Data scientists need to be equipped with skills to tackle the complexities of the societal context surrounding their data science work. In this paper, we offer insights into how to structure our data science classes so that they motivate students to deeply engage with material about societal context and lean into the types of conversations that will produce long lasting growth in critical thinking skills. In particular, we describe a novel assessment tool called participation portfolios, which is motivated by a framework that promotes student autonomy, self reflection, and the building of a learning community. We compare student participation before and after implementing this assessment tool, and our results suggest that this tool increased student participation and helped them move towards course learning objectives.