Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Activity Context <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> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Activity 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> Activity Context <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> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Activity Context <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> BIB004 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Activity Context <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> BIB005 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Activity Context <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> BIB006
Many systems rely on explicit user interactions to capture information about the activity context. These interactions can be scanning a QR or RFID tag BIB006 or providing manual text input-such as the current line number during reading BIB002 or the current topic of interest BIB001 . The current task of the learner is in most cases entered manually by the teacher or the learner. APOSDLE BIB005 and C-LINK  are examples of systems that infer the activity context from user interactions with tools and resources. For instance, APOSDLE uses a classification algorithm to detect the current task of the learner, based on a task model that is created by experts BIB004 . The algorithm uses among others the window title of the active application to infer information about the current task. In addition, a domain model is used to detect the current topic of resources. C-LINK  and Khribi et al. BIB003 extract terms from currently visited resources to infer the current topic of interest. The advantage of the approach is that it can be applied to other domains without the need for a domain taxonomy.