Instructional Tools for Coding
- JupyterHub: Getting Started (Main Article)
- JupyterHub Instructor Guide
- JupyterHub Student Guide
- Instructional Tools for Coding: Ed Discussion (Main article)
- Instructional Tools for Coding: Ed Lessons and Workspaces (Main Article)
To request a consultation on how instructors can use these tools as part of a course, email firstname.lastname@example.org.
Training for instructors, instructional technologists, and students is offered each semester. Search the NYU Events Calendar for "Instructional Tools for Coding" to see current offerings.
The following table can help you explore which NYU-supported tool best meets your teaching needs.
Note: JupyterHub and Ed Discussion, Lessons & Workspaces are part of the Instructional Tools for Coding service described above. Gradescope and Google Colab are not part of this service but are available to NYU instructors with support via their respective non-NYU resources.
|Hosting and Sharing Course Material||
Users have permanent storage.
Shared directories exist for faculty-provided large files.
A Github sync is available.
Faculty can upload files to the Lessons tool for students to view and download.
Workspaces can hold shared notebooks.
|N/A||Faculty or students use their NYU Google storage to control sharing.|
|Assessment & Grading||
Faculty can use nbgrader for assessing students and autograding results.
Faculty can build their own pipeline to tools such as Gradescope.
Ed Lessons includes code challenges that can be autograded.
Results can be passed back automatically to Brightspace.
Assignments tool can be used to host autograded coding assignments.
Results can be passed back to Brightspace.
|In-class coding activities||
Students can utilize JupyterHub during class.
Collaborative notebook features are in testing for pair coding exercises.
Workspaces allows students to work in notebooks alone or together.
Faculty or TAs can join workspaces for support.
|N/A||Students can operate in their own notebooks during class.|
|Advanced computer & data science infrastructure||
Faculty control their JupyterHub instance and can:
• Add custom packages
• Use tools like Keras & Tensorflow
• Attach databases
• Use GPUs
|Ed Lessons provides several challenge types like web dev or database challenges.||N/A||Individual users must set up their own environments|
|Getting support for the tool||Yes. Data Services and RIT* provide consultations as well as live and self-service training options.||Yes. RIT* hosts vendor training each semester for faculty.||
RIT* hosts consultations and vendor-led training.
Self-service support in the NYU ServiceLink knowledge base.
|Google Colab community resources (non-NYU)|