  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.
