Helping Social Studies Teachers to Design Learning Experiences Around Data: Participatory Design for New Teacher-Centric Programming Languages

Social studies educators often use stories to convey historical changes over time. Data visualizations are powerful tools that can help illustrate and investigate these stories and the questions surrounding them. The ability to analyze, interpret, evaluate, and use data and data visualizations - known as data literacy - has become an increasingly important component of K-12 social studies education in recent years. In an effort to improve data literacy, teachers see a role for computer science in social studies education. In Spring 2019, we conducted a pilot participatory design session with 18 social studies pre-service educators with little to no programming experience. This was a unique session where we asked pre-service teachers taking a course on data literacy to inform the design of programming languages, specifically for social studies education. We structured our session as participatory design [1], a novel method in computing education research, where teachers evaluated two programming experiences and then told us what they would like in future programming languages and experiences. The Minimal Manual [2] was used as a guide in the design of the programming activity sheets. Survey results showed teachers initially did not believe programming would be useful, but afterwards their opinions changed and they even showed eagerness to learn and do more with programming for their purposes. The characteristics that the teachers most valued were features of the programming environment, rather than the language syntax or semantics. For their students, they preferred a programming language that would be useful in other classes as well as in social studies. We are currently using conjecture maps [3] to process and analyze the data from our participatory design session. Conjecture maps are meant to specify theoretically salient features in the learning environment design by mapping out how they are predicted to work to produce desired outcomes. It has enabled us to see where and how we can improve future participatory design sessions.