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(hub-types:education)= | ||
# Education and teaching | ||
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The 2i2c Educational Hubs provide learning environments and infrastructure that is meant for teaching data science. | ||
These hubs are inspired by 2i2c's experience with the [DataHubs at UC Berkeley](https://docs.datahub.berkeley.edu/en/latest/) and the [Syzygy service](https://syzygy.ca/) for Canada. | ||
See the sections below for a description of these hubs and how they are used. | ||
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## A brief overview of learning hubs | ||
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This hub deployment is designed for distributed learning for students with a variety of backgrounds. | ||
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Below is a diagram that showcases some of the major components of this hub: | ||
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% automatically downloaded in conf.py | ||
```{figure} /images/collaborative_learning_hub.png | ||
A high level overview of major components in a collaborative learning hub. | ||
``` | ||
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And here are a few major aspects of these distributions: | ||
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Environment | ||
: By default this hub comes iwth a data science environment that covers most introductory workflows in Python and R. | ||
It has been inspired by [the Data 8 course at UC Berkeley](http://data8.org/), and has both Python and R environments for learning. | ||
However, you may also [create your own environment image](environment:image) for your community. | ||
This can then be paired with auto-grading infrastructure such as [Otter Grader](https://otter-grader.readthedocs.io/), which makes grading large courses much easier. | ||
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Content | ||
: The hub comes integrated with [nbgitpuller](https://jupyterhub.github.io/nbgitpuller), which allows you to distribute content that is based online in GitHub repositories. | ||
This can be paired with tools like [Jupyter Book](https://jupyterbook.org) to provide online textbooks for your hub. | ||
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Interfaces | ||
: This hub comes with both JupyterLab, Jupyter Notebook, and RStudio interfaces by default. | ||
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Cloud infrastructure | ||
: The cloud infrastructure needed for this hub is relatively minimal, and defaults to **2GB RAM** and **1 CPU** per user. | ||
This is usually sufficient for most introductory courses. | ||
It can be increased by Hub Administrators, though this will lead to higher cloud costs. | ||
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## A common workflow | ||
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Below is a common workflow that communities use with this type of hub: | ||
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- **Use Jupyter Book for course textbooks**. This allows you to keep all of your source material in markdown files and Jupyter Notebooks, and hosted online as a book for your course. | ||
- **Use `nbgitpuller` links to distribute content to your students**. To distribute worksheets, labs, etc to students, use [nbgitpuller.link](https://jupyterhub.github.io/nbgitpuller/link) to create nbgitpuller links for your content. Clicking these links will give a student their own version of the content that they may modify and save. | ||
- **Use "launch buttons" to connect your book to your hub**. Jupyter Book allows you to [connect your book with a JupyterHub](https://jupyterbook.org/interactive/launchbuttons.html), which students may use to launch directly into your hub from the textbook. | ||
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## Inspiration for this hub | ||
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Collaborative learning hubs are inspired by 2i2c's experience running the [DataHubs at UC Berkeley](https://docs.datahub.berkeley.edu/en/latest/), which provide interactive environments for thousands of students across campus. | ||
You can find the [online textbook for the course here](https://inferentialthinking.com). | ||
They are also inspired by our experience with the [Syzygy service](https://syzygy.ca/) for Canada, which runs JupyterHubs for institutions across Canada. |
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