This project developed curriculum and pedagogical methods for computing in engineering education, based on contextualized learning modules using Python and Jupyter. Python has become the lingua franca of data science, but has had slow adoption in engineering education. Jupyter, the open-source ecosystem for creating computational narratives and data analysis workflows, emerged as an enabling technology for teaching and learning computational materials. The PI of this project is an early adopter of Jupyter for teaching engineering students, and developed open educational resources for courses in computational fluid dynamics and numerical methods for engineering. In this project, her team developed a two-year curriculum for first- and second-year science and engineering students, covering the foundations of technical computing, exploratory data analysis, basic statistics, linear algebra, differential equations, and Fourier analysis. The project also created opportunities for dissemination of the materials and methods, notably with a multi-day faculty development workshop that brought participants from around the country (https://engineerscode.github.io/facultydev1/). The PI also organized a book sprint during which a fifteen-author team co-wrote the open book “Teaching and Learning with Jupyter,” which is recognized as a seminal work that anchors many related efforts. The curriculum was evaluated in the classroom, in particular with respect to changes in student attitudes towards computing, and also as to whether an open-source teaching ecosystem influenced students’ outlook regarding collaboration. The PI presented at conferences such as the Open Education Global Conference 2018 ("A Qualitative Study of Open Educational Practice using Jupyter Notebooks”), the Open edX Conference 2018 ("Jupyter-based courses in Open edX: authoring and grading with notebooks”), the Scientific Python Conference (SciPy: Engineers Code: re-usable, open educational modules for engineering undergraduates”), and JupyterCon, where she organized a session titled "Flipped learning with Jupyter: Experiences, best practices, and supporting research.” For two summers, she taught a session for talented Hispanic high-school juniors at the GW Cisneros Institute “Caminos al Futuro” program. She also developed and facilitated a faculty workshop titled "Hybrid to online with Jupyter-first course development,” University at Buffalo, 2019. At the George Washington University, the PI partnered with the Library to bring her foundational curriculum across the university, via Python camps taught by the data services librarians. These events are held several times per year and are filled up in just days, with long waiting lists, in view of the high demand for such informal training. The PI also worked with the Library to provide a central Jupyter server (JupyterHub) for the university community, which now supports several courses across schools. Together with others in the growing community of faculty teaching with Jupyter, she founded The Journal of Open Source Education, to provide a venue for instructors to publish their contributions in the form of open educational resources and open-source software for educational applications.

All the materials developed in this project are available under open licensing models, at https://github.com/engineersCode

Publications of note: 

- Barba, L.A., 2020. Engineers Code: reusable open learning modules for engineering computations. Computing in Science & Engineering, 22(4), pp.26-35. doi:10.1109/MCSE.2020.2976002 Preprint on arXiv::2001.00228

- Barba, Lorena A.; Barker, Lecia J.; Blank, Douglas S.; Brown, Jed; Downey, Allen; George, Timothy; et al. (2022). Teaching and Learning with Jupyter. figshare. Online resource. https://doi.org/10.6084/m9.figshare.19608801.v1 and online at http://go.gwu.edu/jupyter4edu – Read more on the Jupyter Blog (Jan. 7, 2019), https://blog.jupyter.org/teaching-and-learning-with-jupyter-c1d965f7b93a

Last Modified: 06/18/2023
Modified by: Lorena A Barba