Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context in TEL <s> Describes systems that examine and react to an individual's changing context. Such systems can promote and mediate people's interactions with devices, computers and other people, and they can help navigate unfamiliar places. We believe that a limited amount of information covering a person's proximate environment is most important for this form of computing, since the interesting part of the world around us is what we can see, hear and touch. In this paper, we define context-aware computing and describe four categories of context-aware applications: proximate selection, automatic contextual reconfiguration, contextual information and commands, and content-triggered actions. Instances of these application types have been prototyped on the PARCTAB, a wireless palm-sized computer. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context in TEL <s> Abstract Context is a key issue in interaction between human and computer, describing the surrounding facts that add meaning. In mobile computing location is usually used to approximate context and to implement context-aware applications. We propose that ultra-mobile computing, characterized by devices that are operational and operated while on the move (e.g. PDAs, mobile phones, wearable computers), can significantly benefit from a wider notion of context. To structure the field we introduce a working model for context, discuss mechanisms to acquire context beyond location, and application of context-awareness in ultra-mobile computing. We investigate the utility of sensors for context-awareness and present two prototypical implementations — a light-sensitive display and an orientation-aware PDA interface. The concept is then extended to a model for sensor fusion to enable more sophisticated context recognition. Based on an implementation of the model an experiment is described and the feasibility of the approach is demonstrated. Further, we explore fusion of sensors for acquisition of information on more sophisticated contexts. <s> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context in TEL <s> Context-aware computing is a mobile computing paradigm in which applications can discover and take advantage of contextual information (such as user location, time of day, nearby people and devices, and user activity). Since it was proposed about a decade ago, many researchers have studied this topic and built several context-aware applications to demonstrate the usefulness of this new technology. Context-aware applications (or the system infrastructure to support them), however, have never been widely available to everyday users. In this survey of research on context-aware systems and applications, we looked in depth at the types of context used and models of context information, at systems that support collecting and disseminating context, and at applications that adapt to the changing context. Through this survey, it is clear that context-aware research is an old but rich area for research. The difficulties and possible solutions we outline serve as guidance for researchers hoping to make context-aware computing a reality. <s> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context in TEL <s> Computing devices and applications are now used beyond the desktop, in diverse environments, and this trend toward ubiquitous computing is accelerating. One challenge that remains in this emerging research field is the ability to enhance the behavior of any application by informing it of the context of its use. By context, we refer to any information that characterizes a situation related to the interaction between humans, applications, and the surrounding environment. Context-aware applications promise richer and easier interaction, but the current state of research in this field is still far removed from that vision. This is due to 3 main problems: (a) the notion of context is still ill defined, (b) there is a lack of conceptual models and methods to help drive the design of context-aware applications, and (c) no tools are available to jump-start the development of context-aware applications. In this anchor article, we address these 3 problems in turn. We first define context, identify categories of contextual information, and characterize context-aware application behavior. Though the full impact of context-aware computing requires understanding very subtle and high-level notions of context, we are focusing our efforts on the pieces of context that can be inferred automatically from sensors in a physical environment. We then present a conceptual framework that separates the acquisition and representation of context from the delivery and reaction to context by a context-aware application. We have built a toolkit, the Context Toolkit, that instantiates this conceptual framework and supports the rapid development of a rich space of context-aware applications. We illustrate the usefulness of the conceptual framework by describing a number of context-aware applications that have been prototyped using the Context Toolkit. We also demonstrate how such a framework can support the investigation of important research challenges in the area of context-aware computing. <s> BIB004 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context in TEL <s> The development of context-aware applications will require tools that are based on clearly defined models of context and system software architecture. This essay introduces models for each of these, examines the tradeoffs among the different alternatives, and describes a blackboard-based context architecture that is being used in the construction of interactive workspaces. <s> BIB005 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context in TEL <s> The emergence of ubiquitous computing as a new design paradigm poses significant challenges for human-computer interaction (HCI) and interaction design. Traditionally, HCI has taken place within a constrained and well-understood domain of experience—single users sitting at desks and interacting with conventionally-designed computers employing screens, keyboards and mice for interaction. New opportunities have engendered considerable interest in “context-aware computing”—computational systems that can sense and respond to aspects of the settings in which they are used. However, considerable confusion surrounds the notion of “context”—what it means, what it includes and what role it plays in interactive systems. This paper suggests that the representational stance implied by conventional interpretations of “context” misinterprets the role of context in everyday human activity, and proposes an alternative model that suggests different directions for design. <s> BIB006 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context in TEL <s> The complex usage of mobile devices coupled with their limited resources in terms of display and processing suggests that being able to understand the context of the user would be beneficial. In this paper we present a model that describes context as a dynamic process with historic dependencies. We also describe software architecture to support this model, and evaluate its effectiveness in a mobile learning scenario. Preliminary results from our evaluation suggest important issues for consideration in the continuing development of context aware systems and interfaces, including the need for appropriate representation of contextual data to the user, and maintaining a balance between effective support and intrusion. <s> BIB007 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context in TEL <s> This study investigates the importance of context-awareness and adaptation in mobile learning. Context-aware mobile learning that senses mobile environment and reacts to changing context during learning process has four interaction modes with three mobile entities of different mobility. Contexts in mobile learning are categorized into six dimensions that form a context space. Several mobile learning systems are examined according to the definition of context-aware mobile learning. Challenging issues of context awareness and adaptation are explored. <s> BIB008 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context in TEL <s> In the past years, during the research on ubiquitous learning, we have completely or partially explored three kinds of e-learning systems, PC-based (ECLUE), PDA-based (CLUE+), and mobile phone-based (CLUE+1). Based on these research backgrounds, we explore the concept and the construction of ubiquitous learning environment (ULE). ULE is established on the combinations between real world and virtual space, personal space and shared space. Learning in ULE is conducted in the interactions among three essential communicative elements: social human, object in real world, and artifact in virtual space. Learning process is social transfer process between tacit and explicit knowledge. Human-based social network and broad context-awareness are very important for various interactions and social knowledge building in ULE. This paper proposes a five-dimension representation method for context-awareness information description. Additionally, this paper also gives a simple discussion to related learning terms, including blending learning (BL), social and emotional learning (SEL), and service learning (SL), which are contributive to our deeper understanding to learning in ULE. On practical dimension, this paper presents an example model for ULE implementation by integrating three background learning systems. <s> BIB009 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context in TEL <s> Recent advances in context-awareness provide the facility to track people and objects in the real world and to use these as a trigger for digital events to occur. We are interested in how context-aware mobile technologies can be designed to facilitate learning, and in so doing, to support the cycle between situated action in the real world and subsequent objective reflection. However, what it is to contextualize learning remains unclear. Therefore, we delineate emerging constructs of context and explore the issues surrounding the complexity of each for the learning process. We then draw out some implications for research into the design of contextualized learning applications for mobile devices. <s> BIB010 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context in TEL <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> BIB011 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context in TEL <s> This paper presents a context model to support location based cooperative mobile learning. In the model, a standerdised context template with a context middleware has been introduced to investigate some essential issues on a suitable context pattern applicable to cooperative mobile learning. Its integration with the current e-learning system in University of Central England is also included in this paper. <s> BIB012 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context in TEL <s> The definition of context experienced an evolution in the research area of context-aware computing, but still suffers from either generality or incompleteness. Furthermore, many definitions are driven by the ease of implementation. This paper introduces two extensions to available context definitions that provide a natural understanding of this concept to users of context-aware applications and facilitates the engineering of this concept for software developers of such applications. <s> BIB013 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context in TEL <s> The evolution of information and communication technologies in the last three decades has had an impact in all aspects of human activities. Learning has also been subject of these changes. Current research efforts in the field of mobile learning have been in many cases guided by a learner-centered approach. Context awareness and content adaptivity are crucial components in mobile learning environments. One important challenge is how to design and implement technological tools and methods to support them. In order to tackle this challenge, learners' context should be defined. In this paper, we describe our current efforts regarding how to model context in mobile learning activities. We introduce a time dependent context model based on a three pole structure that can be used to design and support context awareness in mobile learning environments. We illustrate its applicability in four different cases where mobile learning activities and implementations have been guided by the use of this model. <s> BIB014 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context in TEL <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> BIB015
One of the most cited definitions of context is the definition of Dey et al. BIB004 that defines context as "any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves." This definition is referenced extensively within various application domains, including by researchers in TEL  , BIB015 . Dourish BIB006 suggests that context has a dual origin: 1) technical and 2) social science based. From a social perspective, Dourish argues that context is not something that describes a setting or situation, but rather a feature of interaction. Researchers in TEL have argued that this user-centered emphasis on factors affecting an activity is precisely what makes this notion of context meaningful for learning BIB010 , BIB007 . From a technical perspective, there is a need to define context in a more specific way as an operational term BIB005 . To operationalize context, researchers have attempted to define context by enumerating categories. Early work of Schilit et al. BIB001 , BIB004 BIB002 added a task category and define the following dimensions: the user, the social environment of the user, tasks, location, infrastructure, physical conditions, and time. Zimmermann et al. BIB013 list individuality, activity, location, time, and relations as fundamental context categories. Individuality is subdivided into four elements: natural entity, human entity, artificial entity, and group entity. This definition is one of the most comprehensive context definitions to date. In TEL, such enumerations have also been proposed as an attempt to define the context of the learner or teacher as an operational term. Many enumerations are defined for mobile learning applications. Berri et al. BIB011 distinguish between technical and learner context elements. The first category deals with the technical aspects of mobile devices and their operational environment, including the capacity and bandwidth of the wireless network, and input (i.e., keypad) and output (i.e., small screen) constraints. The second category defines learner context elements, including aims and objectives of the learner, prerequisites, background, current level of understanding and subject domain. Beale and Lonsdale BIB007 argue that it is essential to capture interactions between the environment, the user, their tasks, and other users. Environment constitutes computing, time and physical context characteristics. Wang BIB008 defines six dimensions: identity, time, facility, activity, location, and community. Derntl and Hummel  define world context that constitutes location and date and time, physical context (persons, books, journals, learning equipment), digital context (e-papers, e-collaborations, e-learning services), device context (hardware, software, connectivity), and learner information context (personal information such as name, expertise and interests and task specific information). The learner information dimension therefore covers both activity and user dimensions of other definitions. Li et al. BIB009 BIB014 , and Hu and Moore BIB012 . Fig. 1 presents how these definitions relate to each other. The columns are derived from elements that researchers have tried to classify as context categories and the rows show the different context definitions. Whereas the terminology and scope of elements differ significantly, there is also a lot of overlap between existing context definitions. The first four definitions from Shilit et al. BIB001 , Chen and Kotz BIB003 , Schmidt et al. BIB002 , and Zimmermann et al. BIB013 are generic definitions of context that have been used in many application domains. The other definitions have been proposed by researchers in TEL. Most of these definitions refer to similar context categories as defined by generic definitions: location, time, computing, user, activity, and social relations. Physical conditions, such as lighting and noise level, are defined less often in a TEL context. In contrast, resource context (including both physical and digital resources that are relevant to the user) is used more often within TEL applications. Although the scope of context categories differs (i.e., some definitions specify only a few categories that cover several elements whereas other definitions attempt to define context categories more precisely), there are many similarities between the context definitions. The alignment in Fig. 1 presents how the existing context definitions relate to each. Such an alignment can be used to identify the meaning of context elements across different context-aware TEL applications and can be used to define mappings between contextual data representations. In Section 3, we present a context framework for learning that unifies the various definitions and that attempts to define context categories and data elements within these categories in a uniform and precise way.