Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Location <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> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Location <s> Abstract Numerous functional and proposed geospatial standards are critically compared with respect to their purpose, their legal status and the data models supported. Special emphasis is laid upon the issue of interoperabilty, i.e. their inherent capacity to work in a distributed environment, thereby extending previous concepts of data exchange. <s> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Location <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> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Location <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> BIB004
In addition to computing context, location context has dominated research on context-aware mobile computing to a large extent BIB001 . Location models have been proposed that capture human-readable and geometric information of objects, including persons and devices, and relationships between objects. These include: 1) proximity of objects within a space, 2) communicative ability, and 3) orientation, that can for instance indicate to which display device a user is looking  . Location contexts that are often referenced by learning applications include classroom, home, and outdoor BIB003 and several variations on these elements  . Some learning applications use more accurate resolution within these categories through a locating sensor, such as Global Positioning System (GPS) or Wi-Fi. Such quantitative models refer to coordinates with two or three dimensions BIB004 . Several standards and specifications have been elaborated to facilitate the exchange of location data. Endeavors in this area are among other driven by the Open GIS Consortium and ISO/TC21  , BIB002 . To our knowledge, their use in TEL applications is limited.