A quality assurance strategy is implemented to improve effectiveness, efficiency and flexibility of learning objects as well as proper storing and retrieval strategies. To realize this goal, the following sections will propose policies, guidelines, evaluations grids and checklists, peer review, automated metadata capturing and verification as well as user data of both statistical and qualitative nature.

We base our initial guidelines on the following definition:

Accordingly, we have organized these strategies into four main groups, namely organizational strategies, and then three strategies inspired by the life-cycle of a learning object, that is from its conception to its use ∕ reuse (adaptations). 

This initial model is the basis for our questionnaire and how to structure strategies into Best Practices. Further, if will help us organize these Q4R Strategies into adoptable and adaptable workflow models showing who is doing what, when, with what and how. So our discussions on assurance strategies will be considered in terms of

• Organizational Strategies
• Q4R strategies relevant before inclusion in the LOR
• Q4R strategies  relevant at the moment of inclusion in the LOR
• Q4R strategies relevant after inclusion in the LOR

To participate in refining this model fill out our questionnaire about Best Practices if you are a Repository owner, and do participate in our Best Practice Propositions Go to Best Practices WIKI

This model has also helped us in elaborating and structuring four sets of competencies associated to the actors involved in assuring the quality of the learning objects in a Repository. The actors are:

• LOR Manager
• LO Author or Designer  (culturally sensitive)
• LO Metadata Indexer
• LO End-user

To learn more about their tasks, please consult  Competencies in the Q4R Instrument Section.

Principle: It is important to understand that although an inclusive set of strategies are presented, an organization does not have to adopt all !