Building a Model for Integrative Computational Social Science Research

Abstract

Abstract Universities are increasingly interested in providing courses that equip students with data science skills and engage experiential learning, particularly in the social sciences. However, these courses can be costly to implement and time-consuming for instructors to develop. This article describes an integrative learning model for teaching computational social science skills to undergraduate students. There are three elements to the model: content delivery through collaborative learning, skill development in an applied lab setting, and student mentorship. I apply this model to an experiential course where undergraduate students learn to create and conduct a national public opinion poll. The model addresses the need for university classes that equip undergraduate students with computational social science skills, and provides a pathway for training student researchers as teaching assistants for future courses. This model builds the research capacity of faculty, graduate assistants, and undergraduates, invests in data science by providing an infrastructure for the collection of large scale data over time, and integrates experiences inside and outside of the classroom. This model is replicable at other institutions and will be of benefit to programs seeking to implement best practices and maximize learning effectiveness through research integration.