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The X5GON project stands for easily implemented freely available innovative technology elements that will converge currently scattered Open Educational Resources (OER) available in various modalities across Europe and the globe.
X5GON's Learning Analytics Machine (LAM) is capable of dealing with multi-lingual collections of OER. We can give you insight into the usage of your resources across different languages, make your content seen across the world and see how your resources are being used in different cultures.
The X5GON LAM Dashboard (models dashboard) is a web dasboard based on the LAM API aiming to show a specific learning use case which is "Exploring in deep the OERs in order to construct a coherent course set of materials".
The code is conceived to be build-less, directly usable from any checkout of the code.
It is implemented using the VueJS framework. Most components are defined in .vue syntax and loaded through http-vue-loader. This approach has one major drawback: since code is dynamically loaded and interpreted, the devtools interaction is not as direct as it could be.
All application-wide state (search results, basket, sequence) is stored in the Store component, especially interactions with the API.
Hacking: it is advised to install vuejs devtools extensions to facilitate debugging. To avoid local file access restrictions (that vary depending on the browser), it is advised to use a local webserver like devd or
python3 -m http.serverto serve the local directory, and access it with the http protocol.
If you enter a query in the form d:NAME the application will fetch data/NAME.json as debug data. It will use it to populate search results, basket and sequence.
Once up and running, here is how it's looking the LAM Dashboard, the Official X5GON LAM Dashboard. Some functionnalities:
- The "OER neighborhood":
- "OREs in the Basket, ready to be exported":
See the open issues for a list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License License. See LICENSE for more information.
Architecture and implementation:
Graphical design:
Some minor contributions were done in order to fit with the LAM API specifications:
- Victor Connes - victor.connes@gmail.com
- Walid Ben Romdhane - @walidbrw - walid_benromdhane@hotmail.fr
- Colin de la Higuera - cdlh@univ-nantes.fr