Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Dimensions <s> The Dublin Core Metadata Workshop Series began in 1995 with an invitational workshop which brought together librarians, digital library researchers, content experts, and text-markup experts to promote better discovery standards for electronic resources. The Dublin Core is a 15-element set of descriptors that has emerged from this effort in interdisciplinary and international consensus building. This is the first of a set of Informational RFCs describing the Dublin Core. Its purpose is to introduce the Dublin Core and to describe the consensus reached on the semantics of each of the 15 elements. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Dimensions <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> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Dimensions <s> Abstract Given the growing use of mobile devices, there is now increasing interest in the potential for supporting the mobile learner. However, there remains much research to be undertaken, to find effective ways of facilitating learning with mobile devices. This paper considers how to support the mobile language learner using a handheld computer. It introduces TenseITS, a language learning environment that adapts the interaction to the individual learner’s understanding, as represented in a learner model constructed during the interaction. It also adapts according to contextual features of the learner’s location that may affect their ability to study – the likelihood that they will be interrupted by others; their general ability to concentrate at that location; and the amount of time they have available for study. <s> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Dimensions <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> BIB004 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Dimensions <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> Context Dimensions <s> The World Wide Web (WWW) is becoming one of the most preferred and widespread mediums of learning. Unfortunately, most of the current Web-based learning systems are still delivering the same educational resources in the same way to learners with different profiles. A number of past efforts have dealt with e-learning personalization, generally, relying on explicit information. In this paper, we aim to compute on-line automatic recommendations to an active learner based on his/her recent navigation history, as well as exploiting similarities and dissimilarities among user preferences and among the contents of the learning resources. First we start by mining learner profiles using Web usage mining techniques and content-based profiles using information retrieval techniques. Then, we use these profiles to compute relevant links to recommend for an active learner by applying a number of different recommendation strategies. <s> BIB006 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Dimensions <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> BIB007 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Dimensions <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> BIB008 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Dimensions <s> Thanks to the public and low cost availability of wireless high speed internet access students are increasingly equipped with mobile internet enabled devices to connect to university services like Learning Management System (LMS). But the applications services like LMS are still unable to adapt themselves to modern mobile devices with restrictions like reduced display size. By recognizing the device and its restrictions it is possible to optimize the LMS interface. Additionally by using device features like position, acceleration sensors, or the camera it is possible to detect the intentions of the user. The context of the user's situation determines which university services are helpful and interesting. Gathering these context information and reasoning on them is the foundation of our context- and service-oriented approach towards a mobile LMS. This enables us to personalize the mobile learning experience with location-sensitive lecture streaming, campus navigation, and ubiquitous features of the whole university computing infrastructure. By using a service-oriented architecture we are able to compose a variety of different university and external services towards a pervasive university. <s> BIB009 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Dimensions <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> BIB010 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Dimensions <s> One of the success factors of Work Integrated Learning (WIL) is to provide the appropriate content to the users, both suitable for the topics they are currently working on, and their experience level in these topics. Our main contributions in this paper are (i) overcoming the problem of sparse content annotation by using a network based recommendation approach called Associative Network, which exploits the user context as input; (ii) using snippets for not only highlighting relevant parts of documents, but also serving as a basic concept enabling the WIL system to handle text-based and audiovisual content the same way; and (iii) using the Web Tool for Ontology Evaluation (WTE) toolkit for finding the best default semantic similarity measure of the Associative Network for new domains. The approach presented is employed in the software platform APOSDLE, which is designed to enable knowledge workers to learn at work. <s> BIB011 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Dimensions <s> This paper presents an augmented paper-based reading system which incorporated digital materials and the reading strategy of scaffolded questioning into printed materials to enhance studentsi¦ English reading comprehension. QR codes were printed on paper prints to provide direct access to digital resources and scaffolded questions, smart phones were used to scan the QR codes and display digital learning materials. The results showed that the reading strategy of scaffolded questioning significantly improved studentsi¦ English reading comprehension, but direct access to digital materials using QR codes did not. Implications of the study shed light on the future development of the system. <s> BIB012 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Dimensions <s> Responsive open learning environments (ROLEs) are the next generation of personal learning environments (PLEs). While PLEs rely on the simple aggregation of existing content and services mainly using Web 2.0 technologies, ROLEs are transforming lifelong learning by introducing a new infrastructure on a global scale while dealing with existing learning management systems, institutions, and technologies. The requirements engineering process in highly populated test-beds is as important as the technology development. In this paper, we will describe first experiences deploying ROLEs at two higher learning institutions in very different cultural settings. The Shanghai Jiao Tong University in China and at the "Center for Learning and Knowledge Management and Department of Information Management in Mechanical Engineering" (ZLW/IMA) at RWTH Aachen University have exposed ROLEs to theirs students in already established courses. The results demonstrated to readiness of the technology for large-scale trials and the benefits for the students leading to new insights in the design of ROLEs also for more informal learning situations. <s> BIB013
Other systems track the location of the user as a basis to take into account physical conditions that are associated with this location-such as CALS BIB005 and TenseITS BIB003 . Noise level is for instance considered as an important criterion to estimate the possible level of concentration. Such physical conditions are used for learning resource recommendations. For instance, if a learner is on a bus where the likelihood of interruption is high, questions may be suggested to assess her knowledge on a previously learned topic that she can easily resume. Weather and lighting are also outlined as relevant physical conditions  , but to our knowledge these elements have not yet been implemented in a prototype. Ten systems consider the time context of the learner. Timestamp data is in most cases used in conjunction with location information to determine where a user is at a specific point in time BIB009 . Other systems work with interval data to contextualize recommendations according to the available study time of the learner BIB005 , BIB003 , BIB004 ,  . The activity context of the user is used by 15 systems. Many systems use the current task of the learner as a basis to suggest resources that are relevant for this task. 3A BIB010 , APOSDLE BIB011 , Berri et al. BIB004 , C-LINK  , Khribi et al. BIB006 , and MOBIlearn BIB002 identify the topic of the activity to adapt recommendations to the current topic of interest. Nine systems capture and use actions of the user, such as a learner scanning an Radio-Frequency IDentification (RFID) tag or a Quick Response (QR) code as a basis to suggest relevant resources. For instance, Teng et al. BIB012 use this approach to bridge paper-based learning with online learning. More specifically, they recommend digital resources related to a text fragment or scaffolded questions the user is reading on paper. Like traditional User x Item recommender systems for TEL that were described in Section 2.1.2, many systems rely on profiles of resources and users. In addition to general information like author, title, and keywords, both technical and educational characteristics of resources are often described. Also annotation metadata that capture comments of users are used. The IEEE LOM  standard is used by four systems to describe these elements. DEPTHS BIB008 relies on Dublin Core BIB001 . The learner profile captures in many cases the knowledge level of the learner in addition to her interests and preferences. Two systems consider the learning style of the learner. Finally, social relations are used by nine recommender systems. DEPTHS BIB008 relies on the assumption that peer helpers who have already a close connection to the learner will be more likely to help a learner with an activity. These relations are represented using the FOAF  specification and used to suggest peer helpers. Chen and Chao BIB007 developed a system that connects traditional books with a web-based discussion forum. Learners receive messages from an online learning community based on their reading status, which they report through their mobile device. These messages are aimed to increase motivation and include links to additional learning resources. Within the Learning in Process (LIP) project  , a context-aware recommender system was developed to suggest courses, learning resources and peer learners based on roles of users in a corporate environment. The approach is also deployed by APOSDLE BIB011 to support recommendation of learning resources and peers. The 3A recommender BIB010 targets context-aware recommendation in personal learning environments BIB013 . Context is measured and represented by different types of relations, including social relations and relations between resources. We elaborate in the next section how these contextual data are acquired.