Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Discussion <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> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Discussion <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> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Discussion <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> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Discussion <s> Provision of personalized recommendations to users requires accurate modeling of their interests and needs. This work proposes a general framework and specific methodologies for enhancing the accuracy of user modeling in recommender systems by importing and integrating data collected by other recommender systems. Such a process is defined as user models mediation. The work discusses the details of such a generic user modeling mediation framework. It provides a generic user modeling data representation model, demonstrates its compatibility with existing recommendation techniques, and discusses the general steps of the mediation. Specifically, four major types of mediation are presented: cross-user, cross-item, cross-context, and cross-representation. Finally, the work reports the application of the mediation framework and illustrates it with practical mediation scenarios. Evaluations of these scenarios demonstrate the potential benefits of user modeling data mediation, as in certain conditions it allows improving the quality of the recommendations provided to the users. <s> BIB004 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Discussion <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> BIB005 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Discussion <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> BIB006 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Discussion <s> Thanks to the public and low cost availability of wireless high speed internet access students are increasingly equipped with mobile internet enabled devices to connect to university services like Learning Management System (LMS). But the applications services like LMS are still unable to adapt themselves to modern mobile devices with restrictions like reduced display size. By recognizing the device and its restrictions it is possible to optimize the LMS interface. Additionally by using device features like position, acceleration sensors, or the camera it is possible to detect the intentions of the user. The context of the user's situation determines which university services are helpful and interesting. Gathering these context information and reasoning on them is the foundation of our context- and service-oriented approach towards a mobile LMS. This enables us to personalize the mobile learning experience with location-sensitive lecture streaming, campus navigation, and ubiquitous features of the whole university computing infrastructure. By using a service-oriented architecture we are able to compose a variety of different university and external services towards a pervasive university. <s> BIB007
Existing work in recommender systems BIB004 ,  identifies a subtle difference between various parameters of contextual information. They refer to multidimensional spaces for representing contextual variables, and identify mainly three categories/dimensions within which variables may be grouped (i.e user, item, context). Nevertheless, the challenge is reaching towards an identification of variables, as well as their grouping in categories accepted by all researchers and developers. For instance, one could argue that physical conditions are part of location variables, or at least strongly linked to them. Or that learner models are actually user models that are not related to the context. To this end, reaching a generalization that will incorporate all single-or multidimensional representations in a single model can be considered unrealistic. Rather, one could see as a possible line of future work ways to map/link the various representations among them, so that contextual data may be exchanged among different systems. In Section 5 on future challenges, this is discussed under the prism of a Linked Data approach. Fig. 3 summarizes the context elements that are used by the context-aware recommender systems we surveyed. Nine out of 22 systems consider the computing context of the user to suggest suitable learning resources according to hardware, software or network constraints. As illustrated in Fig. 4 , Zhao et al.  rely on WURFL  descriptions of mobile devices and COLDEX BIB001 uses the CC/PP W3C recommendation  . The other systems rely on ad hoc representations of the computing context. Relying on existing standards and specifications offers interesting perspectives to extend, exchange and reuse computing related data for new and different application contexts. The location context of the user is taken into account by ten systems. Lehsten et al. BIB007 detect location as a basis to infer whether the learner is attending a lecture. If she is not attending, a stream of the lecture or other relevant learning resources are recommended. Seven systems sense the location of the user and nearby objects to support situated or collaborative learning activities. For instance, Rogers et al.  , and Zhou and Rechert  use location to suggest learning resources during outdoor learning activities. In MOBIlearn BIB002 , location is detected for generating recommendations of both relevant learning resources and peers who are nearby in a museum. PERKAM BIB005 also uses location context to suggest learning resources and peer learners who are nearby. The system visualizes the distance to potential peer helpers. Another set of context-aware recommender systems uses location context to support language learning. TANGO BIB003 uses nearby objects to suggest learning resources. For example, when the learner enters a meeting room, the system asks her where the remote control of the air conditioner is. If the learner scans the tag labeled on the remote control, the answer is correct. PALLAS BIB006 incorporates time and location dimensions to send automatic notifications to the learner when she is in the vicinity of a point of interest, such as a French art exhibition. In addition, related learning resources are recommended, such as resources related to a building.