Bart Massey 2023

These are tools for working with Canvas grades.

• unpack-hw.py: Download an assignment from Canvas that is presumed to be one ZIP file per submission, unpack that locally for local grading.

  With the --rust flag enabled, Cargo projects in submissions are cleaned up to a more consistent format.

• upload-grades.py: Extract the local grades and upload them to Canvas.

• update-grades.py: Extract the local grades and update a local CSV gradebook. Not used in a long time — may not work.

Thus is SpeedGrader avoided.

• https://canvas.pdx.edu/profile/settings to generate Canvas API token
• Store Canvas API token in ~/.canvasgrader
• Clone canvasgrader fork http://github.com/BartMassey-upstream/canvasgrader (for now) and run pip install .

• Go to https://canvas.pdx.edu/courses/<course-id>/students and copy-paste the JSON found there into students.json (gross, will fix later)

• Download a zipball of the HW from Canvas.

• Run unpack-hw.py --hw <assignment name> <zipball>

• For each directory in staged/:

  • Edit GRADING.txt. Make sure to replace the - with a score.
  • Move directory from staged/ to graded/

• Find course ID in course URL
• Find assignment ID in assignment URL
• Run upload-grades.py <courseid> <asgid>

Note that the --test argument can be used to upload just one grade. This is useful to avoid spamming the list if something is configured wrong / you are unsure.

Thanks to the authors of the Python canvasgrader package for making this all possible.

This work is licensed under the "MIT License". Please see the file LICENSE.txt in this distribution for license terms.