Cloud Computing in the Classroom

Project Summary

OIT is seeking to better understand how faculty use data science notebooks and computational environments, for statistics, collaborative calculations, or machine learning (ML) models, in their classes. These data science workflows can include tools such as Jupyter, Python, R, and more. OIT aims to improve service offerings and support to the university in a scalable way.

Process/Approach 

OIT is reviewing ServiceNow tickets relevant to Cloud Computing in the Classroom and Research Computing, with data compiled by Research Computing during the semesters from Fall 2020 to Spring 2022, as well as distributing a Qualtrics survey to faculty users from these months. Further insights will be gleaned from research into AAU peer institutions and trends in data science, computational environments, and the wider use and adoption of statistical methods across the university.

Project Findings

• Jupyter notebooks have become a standard offering across every public AAU institution.
• While institutions already offered Jupyter notebooks for research, many have now moved to also offer a teaching service for instructors to adopt in their classes across campus.
• Jupyter notebooks have demonstrated strong potential as a teaching tool for a diverse range of pedagogical content and programming language skills.
• The advantages of a cloud-based Jupyter notebook service instructors noted most frequently are interacting remotely with a standardized computational environment, pre-provisioned with course material, accessible from any web browser, requiring no user set up, maintenance, or transferring files.
• Faculty reported using Jupyter notebooks for a range of pedagogical use cases, such as, in class teaching and class demos, practical assignments, accessing real data from industry or labs, as well as independent student work to develop their own analysis tools, run calculations, and simulations, all in cloud-based notebooks.

Project Participants