Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Representation Challenges <s> In a dynamic heterogeneous environment, such as Pervasive Computing, context-aware adaptation is a key concept to meet the varying requirements of different clients. In order to enable context-aware adaptation, context information must be gathered and eventually presented to the application performing the adaptation. Therefore, a common representation format for the context information is required. In this paper we examine possible representation formats for context information. We discuss requirements for such a representation format and how those requirements are met by existing approaches. Based on our findings about the flaws of existing approaches we propose a novel representation format that is comprehensive and thoroughly structured to meet all the requirements: Comprehensive Structured Context Profiles (CSCP). CSCP is based on the Resource Description Framework (RDF) and overcomes the shortcomings of the Composite Capability/Preference Profiles language (CC/PP) regarding structuring. Furthermore it extends the mechanisms to express user preferences. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Representation Challenges <s> The complex usage of mobile devices coupled with their limited resources in terms of display and processing suggests that being able to understand the context of the user would be beneficial. In this paper we present a model that describes context as a dynamic process with historic dependencies. We also describe software architecture to support this model, and evaluate its effectiveness in a mobile learning scenario. Preliminary results from our evaluation suggest important issues for consideration in the continuing development of context aware systems and interfaces, including the need for appropriate representation of contextual data to the user, and maintaining a balance between effective support and intrusion. <s> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Representation Challenges <s> Current e-learning systems fail to take into account the situation of the learner appropriately although there is a big potential for improving those systems. This paper tries to summarize the potentials and highlight the key challenges of context-aware learning systems. It also presents some steps towards mastering the challenges. <s> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Representation Challenges <s> Learning objects repositories have grown and matured in the last years, being currently a cornerstone for open education. Several current systems are offering metadata openly through mainstream harvesting protocols or providing standardized query interfaces. Also, the use of standardized vocabularies or ontologies is becoming more common to provide a degree of semantic interoperability. However, learning object metadata is typically not linked across repositories, and it is not providing a way to navigate by using other sources of data available on the Web. The linked open data (LOD) approach provides the framework for the evolution of learning object repositories into a more flexible system of sharing learning resource metadata. This paper describes how linked data has been integrated in the design and redesign of the export mechanisms of Organic.Edunet, a federation of learning repositories in the domain of organic agriculture that uses an RDF store and several ontologies to browse and search resources. The paper focuses on how the existing search and semantic browsing mechanisms can benefit from the use of LOD across repositories. <s> BIB004
Current recommender systems rely on custom representations of contextual data. Some systems use standardized representations for describing resource and computing characteristics. The lack of a standard representation for contextual data prevents the sharing and reuse of data across systems BIB002 , BIB003 , BIB001 . To address this issue, several solutions have been proposed. Held et al. BIB001 indicate that contextual data should be structured, interchangeable, composable, uniform, extensible, and standardized. In addition, the authors present a novel format, Comprehensive Structured Context Profiles (CSCPs), that is based on RDF. The need for standardization has also been identified by Schmidt BIB003 and Richter and Pawlowski  . Schmidt BIB003 argues that one of the key success factors of technology enhanced learning in the last years has been the standardization activities. The author proposes to include context aspects in the standardization activities, such as SCORM or IEEE LOM. An interesting future line of research would be an integration of both research directions by elaborating a standardized representation of contextual data and mapping those data to existing standards and specifications, so that data compliant to these standards and specifications can be exchanged and reused. The development of a standard representation for both implicit and explicit user data, as well as relevant contextual data that can be associated to these data, will be taken up in a working group of the CEN Workshop on "Learning Technologies" (WS/LT). Initiatives such as LinkedEducation.org have a big potential to interconnect data compliant to existing standards and specifications. The interest in this research area is reflected in several research contributions to a recent workshop on linked learning BIB004 .