Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> User Context <s> When Linda Silverman and I wrote this paper in 1987, our goal was to offer some insights about teaching and learning based on Dr. Silverman’s expertise in educational psychology and my experience in engineering education that would be helpful to some of my fellow engineering professors. When the paper was published early in 1988, the response was astonishing. Almost immediately, reprint requests flooded in from all over the world. The paper started to be cited in the engineering education literature, then in the general science education literature; it was the first article cited in the premier issue of ERIC’s National Teaching and Learning Forum; and it was the most frequently cited paper in articles published in the Journal of Engineering Education over a 10-year period. A self-scoring web-based instrument called the Index of Learning Styles that assesses preferences on four scales of the learning style model developed in the paper currently gets about 100,000 hits a year and has been translated into half a dozen languages that I know about and probably more that I don’t, even though it has not yet been validated. The 1988 paper is still cited more than any other paper I have written, including more recent papers on learning styles. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> User Context <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> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> User 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> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> User Context <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> BIB004 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> User Context <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> User Context <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> BIB006 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> User 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> BIB007
Information about the user is captured in different ways, depending on the type of information. CALS BIB005 and Santos et al.  capture information about learning styles. Both systems use a registration module that enables the user to select her best matching learning style based on the Felder-Silverman BIB001 inventory. Interests of the user are captured explicitly through registration or rating modules  , implicitly through interactions of the user with the system  or by combining both approaches. For instance, C-LINK  and COLDEX BIB002 use a rating module to capture explicit interest indicators and track which resources the user contributes or downloads as implicit interest indicators. PERKAM BIB006 uses a registration module to capture initial interest indicators. These indicators are refined when a learner adds resources to her folder. Such a combined approach is useful to avoid cold-start issues of recommendation algorithms. MOBIlearn BIB003 uses the time the learner spends on a location to estimate interest. Knowledge levels are captured explicitly by some systems. For example, CALS BIB005 requests such information from the learner, whereas TANGO BIB004 relies on teachers to set knowledge levels indicators after examination. TANGO then refines knowledge levels during system use with an implicit approach. For example, when the learner provides correct answers, her knowledge level indicators are adapted. DEPTHS BIB007 also incorporates indicators based on the level of participation in activities. Many systems rely on an implicit approach only through self-assessment tools available in learning environments. COLDEX BIB002 infers the knowledge level of the learner based on the number of resources a learner has downloaded. There are obvious tradeoffs between the approaches: explicit examination by a teacher will provide a more accurate description of the knowledge level than an estimate based on number of downloads. Nevertheless, such estimates may be valuable in scenarios that unobtrusively try to obtain an indication of the knowledge level.