https://github.com/ionmich/autocanvas
Code to automate actions on Canvas using CanvasAPI
https://github.com/ionmich/autocanvas
Last synced: about 1 month ago
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Code to automate actions on Canvas using CanvasAPI
- Host: GitHub
- URL: https://github.com/ionmich/autocanvas
- Owner: IonMich
- Created: 2021-03-27T04:25:29.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2023-01-31T16:10:43.000Z (over 2 years ago)
- Last Synced: 2025-02-14T20:43:33.478Z (3 months ago)
- Language: Jupyter Notebook
- Size: 243 KB
- Stars: 2
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# autocanvas
Code to automate actions on Canvas using CanvasAPI
## Description
The CanvasAPI and its python wrapper provide a powerful framework to automate repetitive actions on the Canvas platform for teachers. In this repository we expand on these methods and we create pipelines to perform a variety of common teaching tasks, such as scheduling quizzes and exams, as well as creating summary statistics and plots. The routines have been designed (but not restricted) to work best with large auditorium courses at the Physics Department the University of Florida. Most of the routines rely on the section information, so courses without student sections will need to use the official Canvas API.
## Getting started
The easiest way to make use of this codebase is by reading the Jupyter Notebooks in the `pipes` and `tests` folders. There you can find a step by step implementation of many pipelines. **NOTE**: before you can run these codes, you will need to install this package.
### Installation Instructions
While notstrictly required, it is recommended that you manage dependenccies for this project by [installing conda](https://docs.conda.io/projects/continuumio-conda/en/latest/user-guide/install/macos.html) and then creating a virtual invironment, by running (replace `` with a name of your choice for the new kernel):
```shell
conda create -n python=3.9
conda activate
```
Next, in order to be able to run the jupyter Notebooks, install ipython kernels:
```shell
conda install ipykernel
ipython kernel install --user --name=
```
Download this package with one of the available methods offred by the platform (http, ssh, or zip file).
Finally, navigate to the directory where `setup.py` resides, and run:
```shell
conda install pip
pip install -e .
```
Check that your installation worked by trying to import the package in a python script or in a python interactive terminal:
```import autocanvas```
If no errors are raised, the installation was successful.
### Creating an API key
The CanvasAPI library gives access to restricted information present on Canvas Course, which is available only to Canvas users with the appropriate permissions (e.g. teaching personel, course designers). As a result, authentication is needed to access this information. This comes in the form of API keys. The official Canvas API documentation page provides a lot of information on how to generate one. As described there, the simplest method of generating one is the following:
1. Go on Canvas and click the "profile" link in the top right menu bar, or navigate to /profile
2. Under the "Approved Integrations" section, click the button to generate a new access token.
3. Once the token is generated, you cannot view it again, and you'll have to generate a new token if you forget it. Remember that access tokens are password equivalent, so keep it secret.
### Storing an API key
In order to avoid having to generate the API key everytime you run the code, store the API key in an environment file named `.env` in the top-level folder (i.e. alongside the `requirements.txt` file). This file should include an entry:
```shell
API_KEY=
```
An example of this file is provided in the repository as `.env.example`. You can rename it to `.env` and then replace `` with your API key (replace the `<` and `>` symbols as well).
## Main Features
The CanvasAPI library returns a `PaginatedList` of python objects after each call to its methods, which are not easy to handle and manipulate. Here we convert these to pandas `DataFrames`, which are ideally suitted for the data processing actions we want perform in Canvas.
Implemented Pipelines:
- Create section overrides for all weekly quizzes.
- Fix the grade of individual questions of a Quiz assignment.
- Create summary plots for weekly quiz grading, grouped by Teaching Assistant (TA).
- Get summary statistics (mean, std) for quiz scores grouped by TA.
- Create quiz overrides for individual students, based on their section.
## Examples
### Monitoring uniformity across graders for a single quiz
See how the grade distributions for a single quiz vary across graders. Example of a violin plot for a single quiz is shown above.
### Monitoring quiz averages
Progression of Quiz averages per TA. The calculation takes into account the fact that the same students are graded by each TA every week, so the sample grades are *not* i.i.d., and therefore the errorbars in the estimated grade average shrink only moderately.
### Monitoring quiz grading progress
Verifying that all quizzes are graded.
## Dependencies
Some of the main dependencies of the code are:
- `selenium`>=3.141.0
- `canvasapi`>=2.2.0
- `pandas`>=1.2.3
- `openpyxl`
- `ipykernel`
- `beautifulsoup4`>=4.9.3
- `lxml`
- `python-dotenv`>=0.17.0
- `aiohttp`
- `seaborn`
- `tabulate`
## Known Issues