Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Data Set Sharing Challenges <s> In an increasing number of scientific disciplines, large data collections are emerging as important community resources. In this paper, we introduce design principles for a data management architecture called the data grid. We describe two basic services that we believe are fundamental to the design of a data grid, namely, storage systems and metadata management. Next, we explain how these services can be used to develop higher-level services for replica management and replica selection. We conclude by describing our initial implementation of data grid functionality. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Data Set Sharing Challenges <s> This article argues that there is a need for Personal Recommender Systems (PRSs) in Learning Networks (LNs) in order to provide learners with advice on the suitable learning activities to follow. LNs target lifelong learners in any learning situation, at all educational levels and in all national contexts. They are community-driven because every member is able to contribute to the learning material. Existing Recommender Systems (RS) and recommendation techniques used for consumer products and other contexts are assessed on their suitability for providing navigational support in an LN. The similarities and differences are translated into specific requirements for learning and specific requirements for recommendation techniques. The article focuses on the use of memory-based recommendation techniques, which calculate recommendations based on the current data set. We propose a combination of memory-based recommendation techniques that appear suitable to realise personalised recommendation on learning activities in the context of e-learning. An initial model for the design of such systems in LNs and a roadmap for their further development are presented. <s> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Data Set Sharing Challenges <s> 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. <s> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Data Set Sharing Challenges <s> One of the success factors of Work Integrated Learning (WIL) is to provide the appropriate content to the users, both suitable for the topics they are currently working on, and their experience level in these topics. Our main contributions in this paper are (i) overcoming the problem of sparse content annotation by using a network based recommendation approach called Associative Network, which exploits the user context as input; (ii) using snippets for not only highlighting relevant parts of documents, but also serving as a basic concept enabling the WIL system to handle text-based and audiovisual content the same way; and (iii) using the Web Tool for Ontology Evaluation (WTE) toolkit for finding the best default semantic similarity measure of the Associative Network for new domains. The approach presented is employed in the software platform APOSDLE, which is designed to enable knowledge workers to learn at work. <s> BIB004 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Data Set Sharing Challenges <s> 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 Each-Movie) 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 learning. 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 improve the performance of recommendation algorithms. <s> BIB005
As drawing conclusions about the validity and generalizability of scientific experiments depends on the possibility of verification, repeatability, and comparisons of results  , a collection of data sets is needed to compare the results of different recommendation algorithms and the influence of contextual data on the recommendation process BIB002 . In an increasing number of scientific disciplines, large data collections are emerging as important community resources BIB001 . A first step toward the identification and sharing of TEL data sets stems from the dataTEL theme team of the STELLAR Network of Excellence BIB003 -recently accepted as an EATEL Special Interest Group (SIG). A special dataTEL Cafe event took place during the RecSys-TEL Workshop 2010  to discuss the submitted data sets and to facilitate data set sharing in the TEL community. Details about the data sets and initial evaluation results of recommendation algorithms with these data sets have been reported in BIB005 . Although interesting data sets have been collected, so far only a few data sets are available that collect data of a large number of users. In addition, most data sets contain only information about learners or teachers and resources. In some data sets time context is captured. Additional context data is available in only one data set that was captured within the APOSDLE project BIB004 . However, the current sample is too small for experimental evaluations. The collection of large data collections that incorporate contextual information about learners is therefore still a major challenge that needs to be tackled. To this extent, a framework is needed that enables 1) to share available data sets from recommender systems in TEL among researchers in this field and 2) to track the outcomes of context-aware recommendation algorithms on these data sets. Additional considerations related to the privacy of users will need to be researched to enable the sharing and reuse of data sets. We discuss this challenge in the next section. The sharing of data sets is an open issue that is recognized by several other people. For example, following the National Science Foundation (NSF, USA), the JISC (United Kingdom) has opened a new research program (2011) (2012) (2013) for the management of research data that "[...] is recognized as one of the most pressing challenges facing the higher education and research sectors. Research data generated by publicly funded research is seen as a public good and should be available for verification and reuse."  .