This is an upgrade to the old learning mastery helper, adding a better UI, better data handling, faster loads and updates, and more options for users to explore and play with data from Canvas.
Canvas provides a method for instructors to attach learning outcomes to assignments and assessments. The rubric mechanism works well, but Canvas lacks powerful tools for evaluating and using that data to make instructional decisions. The Learning Mastery Gradebook provides a high-level overveiew of student performance on assessed outcomes, but seeing individual results and trends is much more difficult.
If you switch to the Individual View in the gradebook, you can click a student name and then use the Learning Mastery tab to see all aligned Outcomes and their scores. This view gets closer to being helpful because it shows scores on assessments over time, which allows you to track progress (growth vs decline) in a chart. To see reports organized by Outcome, you can go to Outcomes and then click on the title of the individual item. This shows the assignments it was assessed on and a list of students who were assessed. This list is not sortable and can be many, many pages long.
There is a lack of consistency in how Outcomes are presented, which amkes them less compelling to use.
The Learning Mastery Helper runs as a standalone web application which can ingest data from Canvas and help instructors and students make informed instructional decisions. There are several benefits provided by using this tool:
- Outcomes, outcome attempts, and assignments can be imported, linked, and explored independently.
- Outcome results can be linked to assignment scores for automatic scoring in the traditional gradebook.
- Users are authorized via the Canvas OAuth sign in flow, meaning the access they have in Canvas carries over automatically into the Helper interface.
- Canvas' scoring options on Outcomes have significant limitations. Instructors can choose one of several custom scoring options to better undersand student performance on Outcomes over time.
Instructions for deploying on a Linux VPS are on INSTALL.md.
If you're interested in contributing, clone the repo and then install all
dependencies with poetry install or pip install -r requirements-dev.txt.