Jehad Najjar

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The information overload in learning and teaching scenarios is a main hindering factor for efficient and effective learning. New methods are needed to help teachers and students in dealing with the vast amount of available information and learning material. Our approach aims to utilize contextualized attention metadata to capture behavioural information of(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)
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)
In this paper, we present the first results of our empirical analysis for the actual use that is made of metadata in Learning Object Repositories, more specifically in the ARIADNE Knowledge Pool System (KPS). We analyze metadata information provided by indexers when they introduce new learning objects into the KPS. This gives a clear indication about their(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 paper outlines how attention metadata enables a tight integration between organisational knowledge stores and human resource management on the one hand and learning management system in corporate contexts on the other hand. The approach relies on an extension of AttentionXML, a metadata standard to capture the attention a user spends on digital content.(More)
This paper investigates the ways in which users interact with Learning Objects Repositories (LORs) such as ARIADNE, MERLOT and SMETE when searching for relevant learning objects. More specifically, we focus on the ways users search the ARIADNE Knowledge Pool System (KPS). Our analysis is based on the log files of queries. We investigate user behavior and(More)