The nascent case for adopting Jupyter notebooks as a pedagogical tool for interdisciplinary Humanities, Social Science, and Arts education
Many societal opportunities and challenges, both current and future, are either inter- or transdisciplinary in nature. Focus and action to cut across traditional academic boundaries has increased in research and, to a less extent, teaching. One successful collaboration has been the augmentation of fields within the Humanities, Social Sciences, and Arts by integrating complementary tools and methods originated from STEM. This trend is gradually materializing in formal undergraduate and secondary education.
The proven effectiveness of Jupyter notebooks for teaching and learning STEM practices gives rise to a nascent case for education seeking to replicate this interdisciplinary design to adopt notebook technology as the best pedagogical tool for this job. This article presents two sets of data to help argue this case.
The first set of data demonstrates the art of the possible. A sample of undergraduate and secondary level courses showcases existing or recent work of educational stakeholders in the US and UK who are already pioneering instruction where computational and data practices are integrated into the study of the Humanities, Social Sciences, and Arts, with Jupyter notebooks chosen as a central pedagogical tool. Supplementary data providing an overview of the types of technical material covered by each course syllabi further evidences what interdisciplinary education is perceived to be or is already feasible using this Jupyter technology with student audiences of these levels.
The second set of data provides more granular, concrete insight derived from user experiences of a handful of the courses from the sample. Four instructors and one student describe a range of pedagogical benefits and value they attribute to the use of Jupyter notebooks in their course(s).
In presenting this nascent case, the article aims to stimulate the development of Jupyter notebook-enabled, computational data-driven interdisciplinary education within undergraduate and secondary school programs.