2025 ASEE Annual Conference & Exposition

Colleges and universities are witnessing the emergence of the new interdisciplinary field of data science/data analytics, which typically includes computing, statistics, mathematics, data systems engineering, and real-world applications. As a new interdisciplinary engineering discipline, the field offers a transformational opportunity to develop a meaningful, collaborative, integrated sociotechnical curriculum from the ground up to bridge the division between “society” and “technology.” Data technologies and data-driven decision-making are a powerful entry point into engineering education for faculty in science and technology studies (STS), engineering studies, and the humanities and social sciences more broadly. All data is constructed through human and social processes, and data analytics and algorithmic systems remix this data to drive future decision-making. Engineering-minded students initially may find it intuitive to draw a box around “the technical system” (say, an algorithm for risk assessment) and separate it from “everything else,” defining their responsibility as optimizing “the technical system.” However, STS provides a toolkit for systematically blurring the boundaries of the box around the “technical system,” showing how considering historical, social, and political settings is essential for competent technical practice.

This paper analyzes UC Berkeley’s experience with ongoing curricular sociotechnical integration and its generative tensions for a team of instructors including STS, Electrical Engineering and Computer Sciences (EECS), and Statistics. The paper presents two case studies of sociotechnical integration in undergraduate technical courses that include practical exercises of applying principles and techniques to real-world situations. In a junior-level data science fundamentals course, a multi-week course unit teaches students linear modeling and feature engineering by way of a real-world case study about the politics of predicting housing prices for property tax assessment. In a senior-level inference and decision-making course, students are asked to complete an integrative course project in which they apply models to draw inferential conclusions about real-world data. An interdisciplinary team of instructors has enriched the course’s existing case studies with STS frameworks to provide students the necessary scaffolding to engage in substantive critical work on final projects.

This paper reflects on the broader goal of building a sociotechnically integrated undergraduate data science curriculum including a dedicated STS class on “human contexts and ethics” and a pedagogical training class. Through these case studies and reflections, the paper shares institutional and interdisciplinary lessons learned from co-designing multiple courses with instructors across disciplines.