There is an increasing use of digital content for learning and education, not only via learning management systems by learners, but also via variety of systems and applications by teachers, managers, reviewers, developers, designers, etc. This usage leads to an explosion of learning resource data, which is not only metadata that describes the characteristics of a learning resource, but also tracking data generated as a result of interaction between learners and resources on learning managements systems, and data about activities/action (currently known as paradata) performed by different kinds of actors on different systems and applications.
Even though this data (of all kinds) contribute together to the learning resource timeline during its lifecycle, it is normally scattered across different and separated systems, and sometimes it is not accessible outside these systems. However, this data could be used, with appropriate learning analytics algorithms, to measure the real quality of learning resources, and to support reusability of these resources by providing statements/assertions about how a learning resource was used before.
To enable sufficient solutions for managing the reusability of learning objects (and learning resources in general), we have developed the CLEAR approach, a new Data-Driven approach that reconsiders the model of Learning Objects in terms of the last advancements, requirements, challenges and trends of eLearning content and systems. CLEAR approach depends on two main new models: Learning Object as a Service (LOaaS), the 1st of its kind; and Linked Learning Registry (LLR), an enhanced model for sharing, integrating, enriching learning resources data.