Creating eight categories of computational and data-centric STEM practices, each course has been designed to leverage notebooks to teach a combination of these tools and methods, within the overall context of application to relevant to various humanistic, societal, or artistic interests. This dataset therefore provides some initial landscape of the example, and often novel, interdisciplinarity education where Jupyter technology has been chosen as the key pedagogical tool for facilitating technical components. For example, seeing at a glance that Jupyter notebooks have been adopted at UC Berkeley to teach undergraduates how to build machine learning models and apply them to the study of law.
This small sample of early Jupyter adopters is still able to signal the types of novel, pathbreaking teaching that has been designed and delivered as we ascertain different combinations of industry-standard computational data tools and methods being applied to particular HSSA area. For example, UC Berkely undergraduates are experiencing novel learning at the intersection of Legal Studies and Mathematical/Statistical Modeling (Index 14) as well as Regulation of Energy and the Environment and Data Visualization (Index 2, including coding interactive widgets).
Without knowledge of how alternatives to Jupyter notebooks perform in the same scenario we of course cannot infer and accurately attribute if this particularly technology is the reason for the courses in the sample being able to design and deliver such technically sophisticated and innovative material. However it does start to build the picture of the developing interdisciplinary HSSA education movement, and tell us about the computational data upskilling aspirations and technology choices of departments, educational institutions, and relevant stakeholders.
The next section provides more conclusive data supporting the article's case as we see certain pedagogical benefits attributed to the course Jupyter notebook usage.