Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Contextual Recommendation Algorithms <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> Contextual Recommendation Algorithms <s> This paper describes a computer supported ubiquitous learning environment for language learning. This paper proposes two systems. The first is context-aware language-learning support system for Japanese polite expressions learning, which is called JAPELAS (Japanese polite expressions learning assisting system). This system provides learner the appropriate polite expressions deriving the learner's situation and personal information. The second system is called TANGO (Tag Added learNinG Objects) system, which detects the objects around learner using RFID tags, and provides the learner the educational information. This paper describes the preliminary evaluation of those two systems. <s> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Contextual Recommendation Algorithms <s> In a cluster of many servers containing heterogeneous multimedia learning material and serving users with different backgrounds (e.g. language, interests, previous knowledge, hardware and connectivity) it may be difficult for the learners to find a piece of material which fit their needs. This is the case of the COLDEX project. Recommender systems have been used to help people sift through all the available information to find that most valuable to them. We propose a recommender system, which suggest multimedia learning material based on the learner’s background preferences as well as the available hardware and software that he/she has. <s> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Contextual Recommendation Algorithms <s> Mobile learning is becoming an important research topic as people are increasingly connected through smart phones that combine telephony, computing, messaging, and multimedia. The process of designing, communicating and presenting learning resources for mobile learners poses new challenges to the research community. This is mainly due to the limited bandwidth of wireless networks, limited resource available on mobile devices, and the special requirements of mobile learners. In this paper, an attempt is made to solve some of these problems by proposing an ontology-based framework for context-aware mobile learning. The system consists mainly of a rule-based ontology and a search agent. The rule-based ontology component is driven by the learner profile to contextualize learning content accordingly. The goal is to extract from a source ontology a lightweight target ontology by firing a set of rules based on the learner profile. The extracted conceptual knowledge is then mapped to a set of learning objects that meet the technical requirements of the used mobile technology. The mapping is geared by a search agent that searches a set of distributed learning objects repositories for feasible lightweight learning objects. <s> BIB004 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Contextual Recommendation Algorithms <s> A learning context describes the current situation of a learner related to a learning activity, and continually changes in a mobile learning environment. As a result, context-awareness in mobile learning has become an essential part when designing more adaptive mobile learning systems. In this paper, we introduce our novel Context- aware and adaptive learning schedule framework which makes use of a learning schedule to support the students' daily routines, adapts the activities to the student's learning styles and then selects the appropriate activity for the learner based on their current learning context. <s> BIB005 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Contextual Recommendation Algorithms <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> Contextual Recommendation Algorithms <s> As it is difficult for learners to discover and obtain the most appropriate resources from massive education resources according to traditional keyword searching method, the context-aware based resource recommendation service becomes a significant part of pervasive learning environments. At present, recommendation mechanisms are widely used in e-commerce field, where content-based or collaborative-based filter strategies are usually considered separately. However, in these existing recommendation mechanisms, the dynamic interests and preference of learners, the access pattern and the other attributes of pervasive learning environments (such as multi-modes connecting and resources distribution) are always neglected. Thus, these mechanisms can not effectively reflect learners' actual preference and can not adapt to pervasive learning environments perfectly. To address these problems, a context-aware resource recommendation model and relevant recommendation algorithm for pervasive learning environments are proposed. Therein, with taking into account the relevant contextual information, the calculation of relevant degree between learners and resources can be divided into two main parts: logic-based RRD (resource relevant degree) and situation-based RRD. In the first part, content-based and collaborative-based recommendation mechanisms are combined together, where the individual preference tree (IPT) is introduced to take into account the multi-dimensional attributes of resources, learners' rating matrix and the energy of access preference. Meanwhile, the learners' historical sequential patterns of resource accessing are also considered to further improve the accuracy of recommendation. In the second part, in order to enhance the validation of recommendation, the connecting type relevance and time satisfaction degree are calculated according to other relevant contexts. Then, the candidate resources can be filtered and sorted via combining these two parts to generate (Top-N) recommendation results. The simulations show that our newly proposed method outperforms other state of-the-art algorithms on traditional and newly presented metrics and it may also be more suitable for pervasive learning environments. Finally, a prototype system is implemented based on SEU-ESP to demonstrate the relevant recommendation process further. <s> BIB007 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Contextual Recommendation Algorithms <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> BIB008 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Contextual Recommendation Algorithms <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> BIB009
As described in Section 2.3, different methods have been proposed to incorporate contextual information in the recommendation process  , including recommendation via context-driven querying and search, contextual prefiltering, contextual postfiltering and contextual modeling. Fourteen systems rely on a recommendation via contextdriven querying and search method (see Fig. 4 ). These systems match contextual data to resource metadata in order to retrieve suitable resources. For instance, MOBIlearn BIB001 matches the location and current topic of interest to descriptions of learning resources and peers. TANGO BIB002 matches the knowledge level of the learner and her location to resource descriptions. Several systems rely on an ontology-based query method to filter suitable resources, including APOSDLE BIB008 and DEPTHS BIB006 . The main difference with traditional recommender systems, discussed in Section 2.1.2, is that additional context data are used to retrieve relevant resources from a repository. Many other systems use a postfiltering algorithm to filter the results of a traditional recommendation algorithm based on contextual information. For instance, COLDEX BIB003 filters the results of a traditional recommendation algorithm based on computing context constraints. PL-CR 2 BIB007 and Berri et al. BIB004 filter the results based on both computing and time constraints. The postfiltering algorithm of CALS BIB005 also takes into account physical conditions. C-LINK  , Zhao et al.  and the 3A recommender BIB009 use a contextual modeling technique that adapts a traditional recommendation algorithm to take into account contextual information. The 3A recommendation algorithm adapts a version of Google's PageRank algorithm to the particular modeling framework that considers relation context. C-LINK  and Zhao et al.  attempt to learn and model user preferences in contextualized user profiles. Such systems adapt traditional algorithms to incorporate multidimensional user preferences, for instance which resources a user liked when she was working on a particular activity. Whereas evaluation results are promising, potential drawbacks and additional challenges related to data sparsity have not been discussed. More generally, there is a need to explore more comprehensively tradeoffs between the different approaches in order to develop a better understanding on which algorithms to use and how to combine them  . So far, only Santos et al.  compared the performance of different combinations of contextual recommendation algorithms. We discuss in Section 5.3 how such evaluations can be facilitated.