Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Resource Context <s> The MOBIlearn project aims to develop a re-usable architecture for delivering mobile learning experiences. A key component of this architecture is a context-awareness subsystem that is intended to tailor the content and options made available to a learner, depending on their current situation, preferences, and learning history. The context awareness subsystem has been developed alongside a hierarchical model of context, and has been subjected to formative evaluation. With reference to our context model, preliminary user trials, and input from museum staff, we describe the planned deployment of this system in an art museum learning scenario. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Resource Context <s> The World Wide Web (WWW) is becoming one of the most preferred and widespread mediums of learning. Unfortunately, most of the current Web-based learning systems are still delivering the same educational resources in the same way to learners with different profiles. A number of past efforts have dealt with e-learning personalization, generally, relying on explicit information. In this paper, we aim to compute on-line automatic recommendations to an active learner based on his/her recent navigation history, as well as exploiting similarities and dissimilarities among user preferences and among the contents of the learning resources. First we start by mining learner profiles using Web usage mining techniques and content-based profiles using information retrieval techniques. Then, we use these profiles to compute relevant links to recommend for an active learner by applying a number of different recommendation strategies. <s> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Resource Context <s> Generating learning object metadata is a complex task to be done manually. An automated approach is therefore required. This work presents the concepts and ideas behind the automatic generation of metadata. We propose a Web Services driven framework for IEEE LOM-compliant automatic learning object annotation called ALOA. The primary focus has been on the flexibility and extensibility of the framework, such that new metadata generation services can easily be plugged into the basic system. <s> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Resource Context <s> One of the main software engineers' competencies, solving software problems, is most effectively acquired through an active examination of learning resources and work on real-world examples in small development teams. This obviously indicates a need for an integration of several existing learning tools and systems in a common collaborative learning environment, as well as advanced educational services that provide students with right in time advice about learning resources and possible collaboration partners. In this paper, we present how we developed and applied a common ontological foundation for the integration of different existing learning tools and systems in a common learning environment called DEPTHS (Design Patterns Teaching Help System). In addition, we present a set of educational services that leverages semantic rich representation of learning resources and students' interaction data to recommend resource relevant for students' current learning context. <s> BIB004 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Resource Context <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> BIB005
Descriptions of resources are often added manually by authors or experts. Such approaches are only suitable for systems that work on a closed corpus of resources. Other systems such as MOBIlearn BIB001 and C-LINK  use annotations of other users as a basis to capture relevant information about resources. C-LINK also automates metadata generation, e.g., by extracting keywords or by using automatic classification methods. Such an approach is also employed by APOSDLE BIB005 , DEPTHS BIB004 , and Khribi et al. BIB002 . Several metadata generation frameworks that integrate a broad set of algorithms to generate metadata have been elaborated in the TEL field  , BIB003 . Such frameworks can potentially be used by some systems that currently rely on manual annotation.