Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Social Relation Context <s> Mobile and ubiquitous learning facilitates language learners to continue their learning process outside the formal classroom, when and where they desire. While more and more learning resources are accessible via a mobile device, there is a challenge in providing access to appropriate personalised learning resources. Personalisation and contextualisation are often used as synonyms. In our work, we distinguish between these two concepts and consider personalisation as a part of contextualisation. This paper describes the PALLAS system which enables real life language learning scenarios by providing personalised and contextualised access to learning resources via a mobile device. The dynamic and static parameters for contextualisation and personalisation of language learning resources are discussed. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Social Relation Context <s> Recent advances in ubiquitous computing technologies have brought reality augmentation of traditional objects to context-aware and social supports. Although a significant proportion of students prefer poring over traditional paper textbooks over electronic books, few studies have enhanced reading practice of traditional books with ubiquitous context-aware and collaborative learning supports that provide timely, contextual assistance. This study proposed an innovative approach to develop a paper-based learning support environment in which mobile phones, traditional books and a Web-based discussion forum are integrated together to promote students' acquisition of knowledge. Students receive contextual messages from an online learning community based on their learning status. The timely recommendatory messages aim to facilitate collaboration among community members and offer guidance in students' study. The findings and results of an evaluation show that students' learning tasks and motivation were supported in the paper-based learning environment by community members. Students also generally had positive attitude to the context-aware recommendatory information delivered via mobile text messages. The ubiquity and immediacy of the recommendations may help students reach the right resources at the right time to improve their learning experience. <s> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Social Relation 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> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Social Relation Context <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> BIB004
Explicit approaches to capture social relations rely on a manual representation of the group structure BIB001 or the organization in corporate environments  . Implicit approaches use for instance enrollment data from learning management systems or social networks BIB002 . Other systems infer social relations by analyzing interactions between users  , BIB003 , BIB004 to obtain indications of the level of collaboration between different members of a group. DEPTHS BIB003 for instance uses such data to suggest peers who can help with a learning activity based on previous collaborations.