This paper investigates basic research issues that need to be addressed in order to reuse learning objects in a flexible way. We propose an ontology based approach. Our ontology for learning objects defines content structures and relationships between their components. A conceptual framework for structuring learning objects and their components is… (More)
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This paper discusses how attention metadata enables collection of rich usage data. We use this attention to enhance users' models, predict usage patterns and feed personalization and recommender systems. We argue that attention metadata extends the information on user attention which can be derived from current log services. Furthermore, frameworks and… (More)
The challenge of finding appropriate learning objects is one of the bottlenecks for end users in Learning Object Repositories (LORs). This paper investigates usability problems of search tools for learning objects. We present findings and recommendations of an iterative usability study conducted to examine the usability of a search tool used to find… (More)
In this paper, we present an approach for producing interoperable metadata by mapping metadata structures of application profiles into standard metadata structures. As a study case, we map the ARIADNE metadata structure into the LOM structure. We use XSLT to transform ARIADNE XML instances into IEEE LOM Instances. Finally, we validate the resulting LOM XML… (More)
The international agricultural research centres of the CGIAR have established a repository for agricultural learning resources in collaboration with the ARIADNE Foundation. To make it easier to find these resources, an application profile, CG LOM Core, was developed on the basis of the IEEE Learning Object Metadata (LOM) standard. It defines the collection… (More)
In this paper we propose a schema and framework for recording and managing attention metadata. This framework is intended to capture, manage, and re-use data about attention users give to learning objects in different applications. Capturing attention allows us to track what objects people use and how they use them.
In this paper, we introduce use cases and standards towards a new approach of learning that is driven by learning outcomes to be achieved by learners. This leads to higher employability of learners.