Jupyter Analytics Dashboard

This repository is part of the learning analytics system (Jupyter Analytics). It builds a JupyterLab extension that communicates with the Backend and provides real-time visualizations of the interaction data collected by Telemetry.

Requirements

• JupyterLab >= 3.1.0

Install

To install the extension, execute:

pip install jupyterlab-unianalytics-dashboard

Uninstall

To remove the extension, execute:

pip uninstall jupyterlab-unianalytics_dashboard

Troubleshoot

If you are seeing the frontend extension, but it is not working, check that the server extension is enabled:

jupyter server extension list

If the server extension is installed and enabled, but you are not seeing the frontend extension, check the frontend extension is installed:

jupyter labextension list

Contributing

Development install

Note: You will need NodeJS to build the extension package.

The jlpm command is JupyterLab's pinned version of yarn that is installed with JupyterLab. You may use yarn or npm in lieu of jlpm below.

# Clone the repo to your local environment
# Change directory to the jupyterlab_unianalytics_dashboard directory
# Install package in development mode
pip install -e ".[test]"
# Link your development version of the extension with JupyterLab
jupyter labextension develop . --overwrite
# Server extension must be manually installed in develop mode
jupyter server extension enable jupyterlab_unianalytics_dashboard
# Rebuild extension Typescript source after making changes
jlpm build

You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.

# Watch the source directory in one terminal, automatically rebuilding when needed
jlpm watch
# Run JupyterLab in another terminal
jupyter lab

With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).

By default, the jlpm build command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:

jupyter lab build --minimize=False

Development uninstall

# Server extension must be manually disabled in develop mode
jupyter server extension disable jupyterlab_unianalytics_dashboard
pip uninstall jupyterlab_unianalytics_dashboard

In development mode, you will also need to remove the symlink created by jupyter labextension develop command. To find its location, you can run jupyter labextension list to figure out where the labextensions folder is located. Then you can remove the symlink named jupyterlab_unianalytics_dashboard within that folder.

Testing the extension

Server tests

This extension is using Pytest for Python code testing.

Install test dependencies (needed only once):

pip install -e ".[test]"
# Each time you install the Python package, you need to restore the front-end extension link
jupyter labextension develop . --overwrite

To execute them, run:

pytest -vv -r ap --cov jupyterlab_unianalytics_dashboard

Frontend tests

This extension is using Jest for JavaScript code testing.

To execute them, execute:

jlpm
jlpm test

Integration tests

This extension uses Playwright for the integration tests (aka user level tests). More precisely, the JupyterLab helper Galata is used to handle testing the extension in JupyterLab.

More information are provided within the ui-tests README.

Packaging the extension

See RELEASE

Credits

• Raphaël Mariétan (main developer)
• Richard Davis (developer, project manager, researcher)
• Zhenyu Cai (developer, researcher)
• Pierre Dillenbourg (principle investigator, research advisor)
• Roland Tormey (research advisor)

This project is part of the "Uni Analytics" project funded by SNSF (Swiss National Science Foundation). That's why in the source code we put "unianalytics" as the identifier. 😃

Citation

If you find this repository useful, please cite our paper:

Cai, Z., Davis, R., Mariétan, R., Tormey, R., & Dillenbourg, P. (2025).
Jupyter Analytics: A Toolkit for Collecting, Analyzing, and Visualizing Distributed Student Activity in Jupyter Notebooks.
In Proceedings of the 56th ACM Technical Symposium on Computer Science Education (SIGCSE TS 2025).

Copyright

© All rights reserved. ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE (EPFL), Switzerland, Computer-Human Interaction Lab for Learning & Instruction (CHILI), 2025

License

This project is licensed under the MIT License. See the LICENSE file for details.