Introduction
Learning Metadata Terms (LMT) is a standard that connects metadata terms in practice with the purpose of solving many use cases common to e-learning. While there are other metadata standards, they have been inadequate for keeping up with machine-readable data requirements, which modern AI needs to achieve significance. While data models attempt to be free of technical bindings, there are fundamental design decisions that relate to whether data is intended to be stored in a graph database or as a record.
Overview of the Standard
The purpose of the standard is to allow both human and machine traceability across properties of any type of learning resource. Because “learning” is so broad, it really can apply to any described learning “object”. Unlike previous metadata standards, LMT differentiates the purpose of the learning object by describing it as either a learning resource or a learning event. A learning resource is anything that is used for learning that is intended to be a shared resource that is almost always digital. The point being that copies of it can be made, and with the right permission, it can be redeployed or first repurposed and then deployed. Learning events, categorized separately, are either instances of learning resources or are resourced opportunities for learning. A common way to put it within the working group was “if you can be late for it, it’s a learning event”.
The standard is extremely relevant for all of the reasons metadata is relevant. By allowing learning resources and learning events to be described, explained, and located, it enables end users of those objects to be more easily connected to them, allows their proper usage, and enables management of learning resources and events. Because learning happens everywhere and encompasses not just knowledge, but skills, abilities, and attitudes, the standard has broad use. In addition, the standard is designed to be fully extensible, with the idea of every “type” of learning event or resource having its own application profile.
Key Features and Benefits
By differentiating learning resources and learning events, defining descriptive data for each now properly contextualizes it. That is, if it is determined a course has an instructor, and that course is a learning event, we know it is that instructor that taught that section of that course at a certain time. Certain students were also in that class. If it was simply a learning resource, it could be the instructor who is usually the person teaching the class. When combining properly contextualized data with paradata such as average grade or a rating, the nature of the difference in data can be more explained by the change of context. E.g., is a course more effective because of the instructor, the platform, or the applied theme?
Figure 1: Contextualized Course Ratings and Aggregate Rating
The prevalent use of URIs in the standard, rather than specific controlled values (think “Netscape Navigator” as a browser choice), allows for a dynamic world where humans and machines can each access data at the URI and receive data back. The uniqueness of URIs means that those “objects” are always unique on the Internet, which greatly helps AI learn. The approach to certain properties also begins to solve problems. One example is the data push/pull problem can be solved by a combination of URIs and using a linked list of related resources rather than simply a version number.
... continue reading