Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> INTRODUCTION <s> Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> INTRODUCTION <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> INTRODUCTION <s> This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations. <s> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> INTRODUCTION <s> Abstract Given the growing use of mobile devices, there is now increasing interest in the potential for supporting the mobile learner. However, there remains much research to be undertaken, to find effective ways of facilitating learning with mobile devices. This paper considers how to support the mobile language learner using a handheld computer. It introduces TenseITS, a language learning environment that adapts the interaction to the individual learner’s understanding, as represented in a learner model constructed during the interaction. It also adapts according to contextual features of the learner’s location that may affect their ability to study – the likelihood that they will be interrupted by others; their general ability to concentrate at that location; and the amount of time they have available for study. <s> BIB004 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> INTRODUCTION <s> This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to recommend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user. <s> BIB005 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> INTRODUCTION <s> Adaptive web sites may offer automated recommendations generated through any number of well-studied techniques including collaborative, content-based and knowledge-based recommendation. Each of these techniques has its own strengths and weaknesses. In search of better performance, researchers have combined recommendation techniques to build hybrid recommender systems. This chapter surveys the space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies. Implementations of 41 hybrids including some novel combinations are examined and compared. The study finds that cascade and augmented hybrids work well, especially when combining two components of differing strengths. <s> BIB006 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> INTRODUCTION <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> BIB007
R ECOMMENDER systems have been researched and deployed extensively over the last decade in various application areas, including e-commerce and e-health. Several recommendation algorithms, such as content-based filtering BIB005 , collaborative filtering BIB001 , knowledge-based filtering  and their hybridizations BIB006 , are widely discussed in the literature and in several surveys of the state-of-the-art BIB003 . Also in the Technology Enhanced Learning (TEL) domain, the deployment of recommender systems has attracted increased interest during the past years. This interest is reflected in the growth of dedicated workshops and special issues on Social Information Retrieval [114] ,  and Recommender Systems  for TEL and is well justified. Whereas Google and other search engines are bound to have a higher recall as they index most of what is available on the Web, their precision for learning is low BIB007 . It is difficult to express a specific learning requirement through keywords. For example, search engines do a poor job when a learner needs content about "relativity theory," oriented to high school level, with a duration of about 30 minutes. Finding relevant resources can be even more difficult when requirements are not always fully known by the learner, such as her level of competence or adequate technical format. Recommender systems for learning try to address these challenges-i.e., they attempt to filter content for different learning settings. A recent survey of recommender systems in TEL has been elaborated by Manouselis et al.  . The authors presented an extensive overview of TEL recommender systems. In addition, evaluation perspectives on current research in this area and future challenges with respect to the evaluation of TEL recommenders were discussed. The notion of context has started to attract significant attention in this research, as indicated by contributions to a recent special issue on Context-Aware Recommender Systems (CARS)  . Among others, advancements of network and mobile services and the growing tool and device landscape provide many new opportunities for the TEL domain to adjust itself to this landscape appropriately  . A new set of recommender systems for learning has been developed in recent years to demonstrate the potential of contextual recommendation. From an operational perspective, context is often defined as an aggregate of various categories that describe the setting in which a recommender is deployed, such as the location, current activity, and available time of the learner. A first example of a context-aware recommender system for learning considers the location of the user and the noise level at this location as a basis to suggest learning resources BIB004 . If the learner is in a cafeteria, the noise level associated to this location might have an impact on her level of concentration and likelihood of interruption. Therefore, a contextual recommender would in such a context suggest learning activities to assess her knowledge on previously learned topics, e.g., through simple questionnaires that she can resume easily at a later point in time. A second example considers proximity between learners to support collaborative learning BIB002 . If a contextual recommender is able to detect people nearby who are working on similar learning activities, the system can suggest suitable peer learners to collaborate with. A third example also takes into account the device that the learner is currently using  . For example, if a learner is being recommended material to study the theory of relativity while commuting from work to school using a smartphone, short, audiovisual material that fits the screen of the smartphone should be more relevant than a long, text-only document. Accordingly, the recommender system should rank the short videos higher than the long documents. This reranking of the recommended resources is not possible if the system does not know about the context of the user. In this perspective, new challenges emerge for capturing and understanding context and exploiting such information for creating intelligent recommendations adapted to current learner needs, without her being necessarily aware of the fact that such contextual variables (e.g., the noise level) are measured and taken into consideration. In this paper, we try to particularly investigate the way in which contextual information may be used by TEL recommender systems, as well as to assess the current maturity of work in this area. The research contribution of this paper is threefold.