Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely diverse and rich environments, the incorporation of contextual information about the user in the recommendation process has attracted major interest. Such contextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs of users, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work in TEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a context framework that identifies relevant context dimensions for TEL applications. Then, we present an analysis of existing TEL recommender systems along these dimensions. Finally, based on our survey results, we outline topics on which further research is needed.
Abstract. In the world of recommender systems, it is a common practice to use public available datasets from different application environments (e.g. MovieLens, Book-Crossing, or EachMovie) in order to evaluate recommendation algorithms. These datasets are used as benchmarks to develop new recommendation algorithms and to compare them to other algorithms in given settings. In this paper, we explore datasets that capture learner interactions with tools and resources. We use the datasets to evaluate and compare the performance of different recommendation algorithms for Technology Enhanced Learning (TEL). We present an experimental comparison of the accuracy of several collaborative filtering algorithms applied to these TEL datasets and elaborate on implicit relevance data, such as downloads and tags, that can be used to augment explicit relevance evidence in order to improve the performance of recommendation algorithms.
RDF-based P2P networks have a number of advantages compared with simpler P2P networks such as Napster, Gnutella or with approaches based on distributed indices such as CAN and CHORD. RDF-based P2P networks allow complex and extendable descriptions of resources instead of fixed and limited ones, and they provide complex query facilities against these metadata instead of simple keyword-based searches.In previous papers, we have described the Edutella infrastructure and different kinds of Edutella peers implementing such an RDFbased P2P network. In this paper we will discuss these RDF-based P2P networks as a specific example of a new type of P2P networks, schema-based P2P networks, and describe the use of super-peer based topologies for these networks. Super-peer based networks can provide better scalability than broadcast based networks, and do provide perfect support for inhomogeneous schema-based networks, which support different metadata schemas and ontologies (crucial for the Semantic Web). Furthermore, as we will show in this paper, they are able to support sophisticated routing and clustering strategies based on the metadata schemas, attributes and ontologies used. Especially helpful in this context is the RDF functionality to uniquely identify schemas, attributes and ontologies. The resulting routing indices can be built using dynamic frequency counting algorithms and support local mediation and transformation rules, and we will sketch some first ideas for implementing these advanced functionalities as well.
This paper raises the issue of missing data sets for recommender systems in Technology Enhanced Learning that can be used as benchmarks to compare different recommendation approaches. It discusses how suitable data sets could be created according to some initial suggestions, and investigates a number of steps that may be followed in order to develop reference data sets that will be adopted and reused within a scientific community. In addition, policies are discussed that are needed to enhance sharing of data sets by taking into account legal protection rights. Finally, an initial elaboration of a representation and exchange format for sharable TEL data sets is carried out. The paper concludes with future research needs
Successful self-regulated learning in a personalized learning environment (PLE) requires self-monitoring of the learner and reflection of learning behaviour. We introduce a tool called CAMera for monitoring and reporting on learning behaviour and thus for supporting learning reflection. The tool collects usage metadata from diverse application programs, stores these metadata as Contextualized Attention Metadata (CAM) and makes them accessible to the learner for recapitulating her learning activities. Usage metadata can be captured both locally on the user's computer and remotely from a server. We introduce two ways of exploiting CAM, namely the analysis of email-messages stored locally on a user's computer and the derivation of patterns and trends in the usage of the MACE system for architectural learning
Peer-to-peer (P2P) networks have become an important infrastructure during the last years. Using P2P networks for distributed information systems allows us to shift the focus from centrally organized to distributed information systems where all peers can provide and have access to information. In previous papers, we have described an RDF-based P2P infrastructure called Edutella which is a specific example of a more advanced approach to P2P networks called schema-based peer-to-peer networks. Schema-based P2P networks have a number of advantages compared with simpler P2P networks such as Napster or Gnutella. Instead of prescribing one global schema to describe content, they support arbitrary metadata schemas and ontologies (crucial for the Semantic Web). Thereby they allow complex and extendable descriptions of resources thus introducing dynamic behavior to the former fixed and limited descriptions, and can provide complex query facilities against these metadata instead of simple keyword-based searches. In this paper we will elaborate topologies, indices and query routing strategies for efficient query distribution in such networks. Our work is based on the concept of super-peer networks which provide better scalability compared to traditional P2P networks. By adapting existing concepts of mediator-based information systems to super-peer based networks, as we will show in this paper, they are able to support sophisticated routing, clustering and mediation strategies based on the metadata schemas and attributes. The resulting routing indices can be built using local clustering policies and support local mediation and transformation rules between heterogeneous schemas, and we sketch some first ideas for implementing these advanced functionalities as well.
Abstract. Open educational resources (OER) have a high potential to address the growing need for training materials in management education and training. Today, a high number of OER in management are already available in a large number of repositories. However, users face barriers as they have to search repository by repository with different interfaces to retrieve the appropriate learning content. In addition, the use of search criteria related to skills, such as learning objectives and skill-levels is not generally supported. The European co-funded project OpenScout addresses these barriers by intelligently connecting leading European OER repositories and providing federated, skillbased search and retrieval web services. On top of this content federation the project supports users with easy-to-apply tools that will accelerate the (re-) use of open content.
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