Project Opus began as the name implies as an internal research project. Our research was focused on how communities can be used to classify and discover content from within large databases or repositories. Donat Group’s previous work in Learning Object Repositories led us to believe that there needed to be a better mechanism than metadata (data that describes the content) for the classification and discovery of content.

What is a Learning Object Repository (LOR)

Essentially a LOR is a big database of learning objects with accompanying metadata made available for educators and learners. This of course begs the question, what is a learning object? Without getting into anything long winded, a learning object is a reusable and self-contained piece of learning material. Or as we like to say in the office, pretty much anything.

The challenge with Learning Object Repositories

Learning Object Repositories required professional librarians to upload, validate, and categorize content. This led to increased costs, occasional bias, and most importantly large bottlenecks in getting content into the LOR, and out to the end users.

Content in an LOR is basically unknown to the majority of the LOR users. As such the quality of the content was suspect. Librarians are not subject matter experts so they cannot assess the actual quality of the content. The time required to assess dozens of learning objects made the whole process unworkable.

Project Opus examined how social networks can be used to describe and discover content. For discovery, we require adequate filters and context for our subject matter

How do social networks describe content?

We learn more about an individual by what she does than from what she writes in her resume. Similarly we know more about a piece of content by how it is used and by whom than by the metadata describing it.

Call it or tag it what you will.
Only use can tell you exactly what it is.

How do social networks filter content?

If we know something about John and Susan, and both John and Susan like and use a piece of content. We know something about that piece of content.

If enough people you trust use a piece of content. You can attest to the quality of that content without ever using it yourself.