Motivating Data Science Students to Participate and Learn

TL;DR

This paper presents a novel participation portfolio assessment tool designed to motivate data science students to engage deeply with societal issues, enhancing critical thinking and participation in class discussions.

Contribution

It introduces a new assessment method that promotes autonomy, self-reflection, and community building in data science education, supported by comparative analysis of student participation.

Findings

Participation increased after implementing the tool.

Students engaged more with societal context topics.

The tool helped achieve course learning objectives.

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.