Preparing Teachers for Teaching Spatial Computational Thinking With Integrated Data Viewer Visualization of Weather Data: A Discipline-Based Perspective of Computational Thinking.

This article focuses on a study evaluating the impact of the 3D Weather project's summer workshop on K-12 teachers' spatial computational thinking (S-CT) skills, self-efficacy, and epistemic cognition in teaching meteorology using the Integrated Data Viewer (IDV) for weather data visualization. The 3D Weather project, funded by the NSF STEM+C program, developed a discipline-based spatial computational thinking model that integrates computational thinking with spatial thinking specific to meteorology, emphasizing the spatial nature of atmospheric data analysis. Nineteen teachers participated in a hybrid workshop combining online and in-person training, after which quantitative survey data showed significant improvements in their spatial computational thinking abilities and self-efficacy for teaching these skills with IDV, while their epistemic cognition favored computational and scientific practices over traditional methods. The study also found no positive correlation between teachers' meteorology content knowledge and their spatial computational thinking or self-efficacy, suggesting complex relationships warranting further research. Limitations include a small, non-representative sample and reliance on quantitative data, with implications for future professional development and research on teacher beliefs and adoption of innovative STEM pedagogies.