Platform Plugin OnTask

Purpose

Open edX plugin that includes adds a new tab to the instructor dashboard for easy access to the OnTask service. Also, it includes an API to create a new workflow and table in OnTask based on the course data. For serving OnTask along with the Open edX ecosystem we recommended using tutor-contrib-ontask.

This plugin has been created as an open source contribution to the Open edX platform and has been funded by the Unidigital project from the Spanish Government - 2024.

Compatibility Notes

Open edX Release	Version
Palm	>= 0.2.0
Quince	>= 0.2.0
Redwood	>= 0.2.0

The settings can be changed in platform_plugin_ontask/settings/common.py or, for example, in tutor configurations.

NOTE: the current common.py works with Open edX Palm, Quince and Redwood version.

Getting Started

Developing

One Time Setup

Clone the repository:

git clone git@github.com:eduNEXT/platform-plugin-ontask.git
cd platform-plugin-ontask

Set up a virtualenv with the same name as the repo and activate it. Here's how you might do that if you have virtualenv set up:

virtualenv -p python3.8 platform-plugin-ontask

Every time you develop something in this repo

Activate the virtualenv. Here's how you might do that if you're using virtualenv:

source platform-plugin-ontask/bin/activate

Grab the latest code:

git checkout main
git pull

Install/update the dev requirements:

make requirements

Run the tests and quality checks (to verify the status before you make any changes):

make validate

Make a new branch for your changes:

git checkout -b <your_github_username>/<short_description>

Using your favorite editor, edit the code to make your change:

vim ...

Run your new tests:

pytest ./path/to/new/tests

Run all the tests and quality checks:

make validate

Commit all your changes, push your branch to github, and open a PR:

git commit ...
git push

Deploying

Tutor environments

To use this plugin in a Tutor environment, you must install it as a requirement of the openedx image. To achieve this, follow these steps:

tutor config save --append OPENEDX_EXTRA_PIP_REQUIREMENTS=git+https://github.com/edunext/platform-plugin-ontask@vX.Y.Z
tutor images build openedx

Then, deploy the resultant image in your environment.

Using the API

The API endpoint is protected with the same auth method as the Open edX platform. For generate a token, you can use the next endpoint:

• POST /<lms_host>/oauth2/access_token/: Generate a token for the user. The content type of the request must be application/x-www-form-urlencoded.

  Body parameters

  • client_id: Client ID of the OAuth2 application. You can find it in the Django admin panel. Normally, it is login-service-client-id.
  • grant_type: Grant type of the OAuth2 application. Normally, it is password.
  • username: Username of the user.
  • password: Password of the user.
  • token_type: Type of the token. By default, it is bearer

  Alternatively, you can use a new OAuth2 application. You can create a new application in the Django admin panel. The body parameters are the same as the previous endpoint, but you must use the client_id and client_secret of the new application. The grant_type must be client_credentials.

  Response

  • access_token: Access token of the user. You must use this token in the Authorization header of the requests to the API.

Finally, you are ready to use the API. The next endpoints are available:

• POST /<lms_host>/platform-plugin-ontask/<course_id>/api/v1/workflow/: Create a new workflow in OnTask. This also creates a new table in the workflow. This endpoint uses the create workflow endpoint from OnTask API.

  Path parameters

  • course_id (Required): ID of the course.

• PUT /<lms_host>/platform-plugin-ontask/<course_id>/api/v1/table/: Updates the current table in a OnTask workflow. This performs a merge of the current table with the new data. This endpoint uses the merge table endpoint from OnTask API.

  Path parameters

  • course_id (Required): ID of the course.

Configuring required in the Open edX platform

You must include the following setting in the LMS to enable the filter that will display add the new tab for OnTask:

OPEN_EDX_FILTERS_CONFIG = {
    "org.openedx.learning.instructor.dashboard.render.started.v1": {
        "fail_silently": False,
        "pipeline": [
            "platform_plugin_ontask.extensions.filters.AddInstructorOnTaskTab",
        ]
    },
}

It is also necessary to include these settings for each course using the Other Course Settings in the Advanced Settings section in Studio:

• ONTASK_API_AUTH_TOKEN (Optional): API Auth token for the OnTask service. You can find it in the OnTask service from Profile > API Auth token. If the token has not been generated, you can create a new one.
• ONTASK_WORKFLOW_ID (Optional): ID of the workflow in OnTask. If you already have a workflow, you can include it here. If you do not have a workflow, the plugin will create a new one from the OnTask tab in the instructor dashboard.

Example:

{
  ...
  "ONTASK_API_AUTH_TOKEN": "your-api-auth-token",
  "ONTASK_WORKFLOW_ID": 1
}

NOTE: It is posible to configure the ONTASK_API_AUTH_TOKEN at platform level. You can include it in the LMS settings. This way, you do not need to configure it in each course.

NOTE: It is important to have enabled the Other Course Settings in the settings of the platform. You can find more information about this in the Open edX documentation.

View from the Learning Management System (LMS)

When the instructor accesses the OnTask tab, they will have the option to create an OnTask Workflow in case there is not one configured. Once the Create Workflow button is clicked, a workflow will be created in OnTask and will be named as the current course ID. e.g. course-v1:edunext+ontask+demo.

The instructor can then view the OnTask workflow related to the course. From there the instructor can create, import/export or execute all actions. In the upper left corner there will be a button called Load Data. Once pressed, an asynchronous task will be executed that will generate a data summary of the course and load it into a table in the OnTask Workflow. Each time the button is pressed, the data will be recreated. The instructor can then use this data to create different actions.

Once the data has been loaded, the instructor can go to Table > View data. There the instructor find the table with all the data summary of the course.

Create a Custom Data Summary Backend

By default, the data summary loaded into the OnTask table is generated from the user, unit completion and component grade data. These are the columns that are loaded into the OnTask table:

• user_id (Integer): ID of the user.
• email (String): Email of the user.
• username (String): Username of the user.
• {short_section_name}> {short_subsection_name}> {short_unit_name} {short_block_id} Completed (Boolean): If the unit is completed.
• {short_unit_name}({short_unit_blockid})> {short_component_name} {short_block_id} Grade (Integer): Grade of the component.

You can create a custom data summary backend to add new columns to the data summary that is loaded into the OnTask table. To do this, follow these steps:

1. Create a new file in the data_summary directory with the name of the backend, e.g., custom.py

2. Create a class that inherits from DataSummary, e.g., CustomDataSummary(DataSummary)

3. Implement the get_data_summary method to return the data summary. The method must return a dictionary where each key is the column name and the value is other dictionary with the ID as key, and the value as the value of the column.

   class CustomDataSummary(DataSummary):
     """Custom data summary for example purposes."""
   
     def get_data_summary(self) -> dict:
       """
       Get a custom data summary.
   
       Returns:
           dict: A custom data summary.
       """
       data_frame = {
           "user_id": {"0": 1},
           "unit (a7e390) > component 841e2 custom_value": {"0": False},
           "unit (a7e390) > component 841e2 another_custom_value": {"0": "value"},
       }
       return data_frame

   NOTE: The dataframe must include at least the user_id column. This is important when merge the data with the current OnTask table.

4. Edit the ONTASK_DATA_SUMMARY_CLASSES setting in the common.py file to include the new backend in the list of backends.

   settings.ONTASK_DATA_SUMMARY_CLASSES = [
     # ...Another data summary backends
     "platform_plugin_ontask.datasummary.backends.custom.CustomDataSummary"
   ]

   NOTE: The UserDataSummary, UnitCompletionDataSummary and ComponentGradeDataSummary are the default data summary backends. If you do not want to use them, you can do so by removing them from the ONTASK_DATA_SUMMARY_CLASSES setting.

Getting Help

If you're having trouble, we have discussion forums at discussions where you can connect with others in the community.

Our real-time conversations are on Slack. You can request a Slack invitation, then join our community Slack workspace.

For anything non-trivial, the best path is to open an issue in this repository with as many details about the issue you are facing as you can provide.

For more information about these options, see the Getting Help page.

License

The code in this repository is licensed under the AGPL 3.0 unless otherwise noted.

Please see LICENSE.txt for details.

Contributing

Contributions are very welcome. Please read How To Contribute for details.

This project is currently accepting all types of contributions, bug fixes, security fixes, maintenance work, or new features. However, please make sure to have a discussion about your new feature idea with the maintainers prior to beginning development to maximize the chances of your change being accepted. You can start a conversation by creating a new issue on this repo summarizing your idea.

Reporting Security Issues

Please do not report security issues in public. Please email security@edunext.co.