PRODUCT

Recommend

Show your content in a network of other sites!

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By joining forces and connecting your OER site to all existing OER sites into the first Global OER Network, we can together unleash the equity potential of OER and start the first data driven effort capable of understanding and recommending OERs across different sites, languages, modalities such as video, documents and textbooks.

Place recommendation modules in your repository content, create new discovery experiences, and engage your audience with the most relevant content from across all of the sites in our OER Network.

We have designed rich models of users on OER sites, and used these models for recommendation and personalization of learning materials. More specifically the user modelling architecture supports real-time, cross-site and cross-lingual user models and recommendation techniques which take into account the user and content meta-data available in online learning environments.

On top of this we have developed a global cross-site and cross-lingual recommendation engine. It uses machine learning techniques for their core, and semantic technologies to ensure valid combinations of recommended materials and existing skills of the user.

Cross-site recommendation uses content models of learning materials to identify related and complementary learning materials between sites. Cross-lingual aspects are handled by using Wikipedia, and comparing text documents across top 100 Wikipedia languages.

Learn more about other our products.

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Analytics

Understand the trends of your content usage

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Discovery

Search and find materials from all over the world

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Translate

Translate your content in every format

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Connect

Connect users with OER sites in Moodle

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Feed

Provide data for all stakeholders via API

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