In academica there has been the emergence of Computational Social Science, Digital Humanities, and STEAM fields of study. Around the world shiny new collaborative institutes, centres, and working groups have popped up, cutting across previously disparate fields to be equipped to explore contemporary matters.
# computer-powered and data-centric (computational data)
These modern Humanities, Social Science, and Arts research collaborations have been harnessing the rich ecosystem of computing and data (computational data) tools and methods previously considered the domain of STEM for a variety of applications, such as exploring vast archives of recently digitised cultural material or mining constantly evolving sources of user-generated content. And many of these researchers are using Jupyter notebooks. A repository of Digital Humanities Jupyter notebooks collected from Twitter demonstrates the range of disciplines at play \cite{quinnanyadh-jupyter}.
There is, therefore, a clear opportunity for education of these evolving disciplines to also reflect developments at the knowledge frontier, to the appropriate degree. Additionally, in so far as education's role for preparing students to participate effectively society, Humanities, Social Science, and Arts curricula needs more interdisciplinarity to reflect the skills and experience learners will need as future citizens.
Again as is seen at research level, Jupyter notebooks can play an important role in modernising teaching of these traditionally less technical areas by making computational data tools and methods more accessible.
Ultimately, in the following sections it will be shown that such Jupyter-enabled Humanities, Social Science, and Arts educational experiences have led to valuable instructor and student outcomes.
This article therefore aims to present examples of Jupyter-based Humanities, Social Science, and Arts teaching, the computational data practices integrated into these courses, and most importantly share direct experiences and feedback from some of the instructors and students of these courses with the aim of encouraging more similar courses and positive outcomes.
# Comp Data Pedagogical value gained from using Jupyter? NHEHS no other option compatible for gifs, true type fonts
Sample of Adopters
# Definition of interdisciplinary HSSA education where Jupyter notebooks used as a tool
The Dataset - Sources, Criteria, Interpretations, Limitations
Through manually searching public data sources (Appendix A) and supplementing with data from my own work I have collated a sample of 20 taught courses relevant to the article's interests from undergraduate and secondary level in the US and UK.
Each course included uses Jupyter to teach computation data tools and methods, which make up a significant proportion of course syllabus, but is unmistakably a means to an end and thus the overall purpose of said technical content is its' application to Humanities, Social Science, and Arts topics. This criteria therefore rules out several Jupyter-based teaching that have either too small-scale computational data focus (such as UC Berkeley courses under the deliberate strategy of initial small-scale Data Science integration \cite{berkeley}), as well as full-scale Data Science courses where computational data practices are the end goal.
Range of Humanities, Social Science, and Arts topics