Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Acquisition Challenges <s> The complex usage of mobile devices coupled with their limited resources in terms of display and processing suggests that being able to understand the context of the user would be beneficial. In this paper we present a model that describes context as a dynamic process with historic dependencies. We also describe software architecture to support this model, and evaluate its effectiveness in a mobile learning scenario. Preliminary results from our evaluation suggest important issues for consideration in the continuing development of context aware systems and interfaces, including the need for appropriate representation of contextual data to the user, and maintaining a balance between effective support and intrusion. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Acquisition Challenges <s> Current e-learning systems fail to take into account the situation of the learner appropriately although there is a big potential for improving those systems. This paper tries to summarize the potentials and highlight the key challenges of context-aware learning systems. It also presents some steps towards mastering the challenges. <s> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Acquisition Challenges <s> Recent advances in context-awareness provide the facility to track people and objects in the real world and to use these as a trigger for digital events to occur. We are interested in how context-aware mobile technologies can be designed to facilitate learning, and in so doing, to support the cycle between situated action in the real world and subsequent objective reflection. However, what it is to contextualize learning remains unclear. Therefore, we delineate emerging constructs of context and explore the issues surrounding the complexity of each for the learning process. We then draw out some implications for research into the design of contextualized learning applications for mobile devices. <s> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Acquisition Challenges <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> BIB004 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Context Acquisition Challenges <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
Many of the surveyed systems rely on manual input from users. Whereas such manual input is useful to evaluate first prototypes, we believe that requesting such involvement from end-users may significantly hamper the uptake of context-aware recommendation for learning. In addition, several systems indicate that they use contextual data, but do not describe the methods that are used to capture these data (see Fig. 3 ). An interesting future direction of research is the development of context sensors for learning that automate the acquisition of the context dimensions in a modular and generic fashion. Such modular approach has been proposed by several researchers, for context-aware systems in general  , and TEL in specific BIB002 . Results of our survey indicate that the biggest challenge lies in capturing the user and activity context dimensions (actions, current topic of interest, tasks). Researchers in TEL have argued that factors affecting an activity is precisely what makes the notion of context meaningful for learning BIB003 , BIB001 . However, in contrast to other dimensions such as computing, location and social relations that have been researched more extensively by the mobile learning and social network analysis communities, these dimensions still rely in many cases on explicit capturing methods. Whereas some promising examples of systems that automate activity context acquisition have been presented, the approaches either rely on task models that are created by experts BIB005 or have only been partially evaluated BIB004 ,  . To address this challenge, there is a need for interdisciplinary collaborative research from different research communities, including the learning analytics, educational data mining, adaptive hypermedia and user modeling communities. In recent years, much progress has been made in these areas to capture data from the learner in all her respective learning environments and to measure learning indicators related to low level activity data  . Learning context sensors for the various dimensions outlined above would enable researchers in the recommender systems for TEL field to reuse existing efforts in this area. As discussed in Section 2.1.2, many existing recommender systems for learning do not yet incorporate additional context dimensions. The availability of context sensors would be of major interest to incorporate contextual information in recommender systems for learning on a much larger scale than in today's prototypes. In addition to the exploration and use of existing techniques to extract contextual data, an interesting future line of research would be to investigate how contextual data can be collected implicitly by using a combination of open data sources and APIs. A simple example combines timestamp and location information with a weather service to suggest situated learning activities. Elaborating such scenarios for both capturing relevant contextual data as well as using such data for generating contextual recommendations constitutes another interesting area of research.