For learning objects recommendation approach a relation between user profile and context features with metadata of educational resources is then established.
Generally, educational resources are cataloged using metadata standards, such as IEEE LOM [57] and OBAA [58], allowing search and reuse of these resources.
Features of educational resources are recovered through the metadata.
For that, an automated classification algorithm is trained using the information extracted from the metadata of a collection of educational resources hosted in different repositories, which in turn depends on the classification taxonomy used in each case.
Essentially, the metadata collected would help educators and learners quickly and efficiently find relevant educational material online.
Its intuitive interface for searching and storing metadata optimizes workflow, and a variety of educational videos assists first-time users familiarize themselves with its interface.
There are four applications currently working on incorporating the CAP metadata – two caBIG™ products, one vendor application, and one research educational application.
BROAD-RSI focus on the recommendation of educational resources stored in its own repository, and cataloged by a set of metadata, as detailed in [56].
Being more complex, these objects have more specific and detailed metadata for the purposes of discovery, reusability and interoperability such as educational objective, prerequisites, topic, interactivity and technology requirements.
These labs can be searched on the basis of an extensive set of metadata that offer direct links, for example, to science curricula, language, educational objectives, level of interaction and difficulty of the lab.
The SkateBase website developed by this project, further acts as a centralized repository for these sequence and annotation data, additional project results and metadata, tools for visualizing and searching these data, project news and educational content (Table 1).
