The main advantage of using learning objects to build learning pathways is modularity. Like Lego bricks, teachers can more easily build flexible learning pathways, adapted to the specific needs or goals of their students.
But how can we identify which learning objects can be interchanged with other? In order to do this we need to have well characterised learning objects, i.e. rich metadata. Metadata are all the data describing a learning objects such as its title, description, thumbnail picture, length, learning goal, etc.
Over the last 20 years several data models for Learning Object Metadata were developed. It is very interesting that there are two types of data models for learning objects:
- education-based: these data model are closely linked to the IT system of educational organisations. For exemple the Dublin Core is a set of 15 elements (properties) for describing resources, mainly inherited from how library classified books.
- search engine-based: these data model are closely linked to the development of internet browsing and webpage indexing by search engine. For example, schema.org is a joint initiative by Google and Baidu (and others) to find a common way to describe web resources.
Both approaches have pros and cons. At Inokufu, we chose to start from the typical search engine metadata model and enrich it to better cover the various type of learning object.
We will present you in the following page all the metadata we collect and provide about the learning object we index.
We are currently working on the interoperability with metadata model such as LHEO or Node-fr.