Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Activity <s> The information overload in learning and teaching scenarios is a main hindering factor for efficient and effective learning. New methods are needed to help teachers and students in dealing with the vast amount of available information and learning material. Our approach aims to utilize contextualized attention metadata to capture behavioural information of users in learning contexts that can be used to deal with the information overload in user centric ways. We introduce a schema and framework for capturing and managing such contextualized attention metadata in this paper. Schema and framework are designed to enable collecting and merging observations about the attention users give to content and their contexts. The contextualized attention metadata schema enables the correlation of the observations, thus reflects the relationships that exists between the user, her context and the content she works with. We illustrate with a simple demo application how contextualized attention metadata can be collected from several tools, the merging of the data streams into a repository and finally the correlation of the data. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Activity <s> In this paper we present a knowledge-driven model for mobile learning based on the semantic Web. The knowledge model uses a global ontology space and a unified reasoning mechanism to integrate and aggregate knowledge describing both system-centric and user-centric context information. The reasoning engine perceives, understands, and translates context changes into new adaptation constraints in the operating environment to achieve personalized learning. In particular, the system strives to adapt the learning sequence and the learning content based on the learnerpsilas activity, profile, used technology, and surrounding environment. An initial system prototype is described and the obtained experimental results are very promising. <s> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Activity <s> 'Understanding context is vital' [1] and 'context is key' [2] signal the key interest in the context detection field. One important challenge in this area is automatically detecting the user's task because once it is known it is possible to support her better. In this paper we propose an ontology-based user interaction context model (UICO) that enhances the performance of task detection on the user's computer desktop. Starting from low-level contextual attention meta-data captured from the user's desktop, we utilize rule-based, information extraction and machine learning approaches to automatically populate this user interaction context model. Furthermore we automatically derive relations between the model's entities and automatically detect the user's task. We present evaluation results of a large-scale user study we carried out in a knowledge-intensive business environment, which support our approach. <s> BIB003
Activity context reflects the tasks, objectives or actions of the user. Examples of models that have been elaborated in a TEL context are the Contextualized Attention Metadata (CAM) BIB001 and the UICO BIB003 models. Both models enable the capturing of actions of the user, which are comprised of events within an application, and session and time related data. Such data is then analyzed to infer information about the current task, objective or topic of interest of the user, for instance by using domain ontologies that describe the subject domain BIB002 . The models provide elements to capture additional contextual data, but do not define specific types of such data.