Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Recommendation Outputs <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> Recommendation Outputs <s> This paper introduces a ubiquitous computing environment in order to support the learners while doing tasks; this environment is called PERKAM (PERsonalized Knowledge Awareness Map). PERKAM allows the learners to share knowledge, interact, collaborate, and exchange individual experiences. It utilizes the RFID ubiquities technology to detect the learner’s environmental objects and location, then recommends the best matched educational materials and peer helpers in accordance with the detected objects and the current location. This environment provides the learner with Knowledge Awareness Map, which visualizes the space of the environmental objects that surround the learner, the educational materials space, and the peer helpers’ space. PERKAM system was implemented and an experiment was done in order to evaluate the system performance and the learner’s satisfaction. This paper illustrates the design, the implementation and the evaluation of this environment, and it focuses on Knowledge Awareness Map, which is personalized according to the learner’s current need and location. <s> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Recommendation Outputs <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> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Recommendation Outputs <s> Mobile and ubiquitous learning facilitates language learners to continue their learning process outside the formal classroom, when and where they desire. While more and more learning resources are accessible via a mobile device, there is a challenge in providing access to appropriate personalised learning resources. Personalisation and contextualisation are often used as synonyms. In our work, we distinguish between these two concepts and consider personalisation as a part of contextualisation. This paper describes the PALLAS system which enables real life language learning scenarios by providing personalised and contextualised access to learning resources via a mobile device. The dynamic and static parameters for contextualisation and personalisation of language learning resources are discussed. <s> BIB004 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Recommendation Outputs <s> Recent advances in ubiquitous computing technologies have brought reality augmentation of traditional objects to context-aware and social supports. Although a significant proportion of students prefer poring over traditional paper textbooks over electronic books, few studies have enhanced reading practice of traditional books with ubiquitous context-aware and collaborative learning supports that provide timely, contextual assistance. This study proposed an innovative approach to develop a paper-based learning support environment in which mobile phones, traditional books and a Web-based discussion forum are integrated together to promote students' acquisition of knowledge. Students receive contextual messages from an online learning community based on their learning status. The timely recommendatory messages aim to facilitate collaboration among community members and offer guidance in students' study. The findings and results of an evaluation show that students' learning tasks and motivation were supported in the paper-based learning environment by community members. Students also generally had positive attitude to the context-aware recommendatory information delivered via mobile text messages. The ubiquity and immediacy of the recommendations may help students reach the right resources at the right time to improve their learning experience. <s> BIB005 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Recommendation Outputs <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> BIB006 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Recommendation Outputs <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> BIB007 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Recommendation Outputs <s> This paper discusses the 3A recommender system that targets CSCL (computer-supported collaborative learning) and CSCW (computer-supported collaborative work) environments. The proposed system models user interactions in a heterogeneous graph. Then, it applies a personalized, contextual, and multi-relational ranking algorithm to simultaneously rank actors, activity spaces, and assets. The results of an empirical evaluation carried out on an Epinions dataset indicate that the proposed recommendation approach exploiting the trust and authorship networks performs better than user-based collaborative filtering in terms of recall <s> BIB008
All surveyed recommender systems offer suggestions for suitable learning resources (see Fig. 4 ). In many cases, these resources are closed corpus resources that were made available and annotated to support the target learning activities, such as outdoor or language learning activities based on available objects. Nine systems enable recommendation of open corpus resources. These systems rely on automatic annotation BIB007 , BIB003 , BIB006 and/or annotations that are made by peers  , BIB001 , BIB008 . Seven systems provide suggestions for suitablepeer learners or teachers in addition to learning resources. These people are suggested based on proximity BIB001 or social relations BIB006 , BIB008 , or a combination of both BIB002 . In addition, some systems include support for recommending messages to guide and motivate the learner. These messages are sometimes automatically triggered based on activity or location context BIB004 or provided manually by teachers or peers BIB005 .