Metadata for social recommendations: storing, sharing and reusing evaluations of learning resources

Abstract

Social information retrieval systems, such as recommender systems, can benefit greatly from sharable and reusable evaluations of online resources. For example, in distributed repositories with rich collections of learning resources, users can benefit from evaluations, ratings, reviews, annotations, etc. that previous users have provided. Furthermore, sharing these evaluations and annotations can help attain the critical mass of data required for social information retrieval systems to be effective and efficient. This kind of interoperability requires a common framework that can be used to describe the evaluation approach and its results in a reusable manner. This chapter discusses this concept, focusing on the rationale for a reusable and interoperable framework, that can be used to facilitate the representation, management and reuse of results from the evaluation of learning resources. For this purpose, we review a variety of evaluation approaches for learning resources, and study ways in which evaluation results may be characterised, so as to draw requirements for sharable and reusable evaluation metadata. Usage scenarios illustrate how evaluation metadata can be useful in the context of recommender systems for learning resources.

status: published