Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Particularities of TEL for Recommendation <s> This article presents the major findings from six regional analyses conducted within the framework of the European Web-edu project (http://www.nettskolen.com/in_english/webedusite/index.html). It analyses the experiences of European institutions with the Learning Management Systems that they have purchased or developed themselves. Data was collected from in-depth interviews with 113 European experts, usually the systems managers in the institutions, in 17 countries. The analyses of the interviews revealed as many as 52 different commercial and 35 self-developed LMS systems. The article presents the data from these interviews and includes a series of important findings from the study. One conclusion is that there is a host of commercial and self-developed systems that seem to work satisfactorily in various educational institutions throughout Europe. The systems are not able to handle all the functions the institutions want, and they can be improved in many ways. But most systems encountered in the analyses seem to be good enough for handling online education successfully. Another conclusion is that the European market is not dominated by the American LMS systems. In countries that do not use English as the first language, locally developed LMS systems have successfully ousted the American products. Remarkably, a large number of the LMS systems used in Europe are commercial systems developed locally, or self-developed systems built by the institutions. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Particularities of TEL for Recommendation <s> Summary Institutions for formal education and most work places are equipped today with at least some kind of tools that bring together people and content artefacts in learning activities to support them in constructing and processing information and knowledge. For almost half a century, science and practice have been discussing models on how to bring personalisation through digital means to these environments. Learning environments and their construction as well as maintenance makes up the most crucial part of the learning process and the desired learning outcomes and theories should take this into account. Instruction itself as the predominant paradigm has to step down. The learning environment is an (if not ‘the’) important outcome of a learning process, not just a stage to perform a ‘learning play’. For these good reasons, we therefore consider instructional design theories to be flawed. In this article we first clarify key concepts and assumptions for personalised learning environments. Afterwards, we summarise our critique on the contemporary models for personalised adaptive learning. Subsequently, we propose our alternative, i.e. the concept of a mash-up personal learning environment that provides adaptation mechanisms for learning environment construction and maintenance. The web application mash-up solution allows learners to reuse existing (web-based) tools plus services. Our alternative, LISL is a design language model for creating, managing, maintaining, and learning about learning environment design; it is complemented by a proof of concept, the MUPPLE platform. We demonstrate this approach with a prototypical implementation and a – we think – comprehensible example. Finally, we round up the article with a discussion on possible extensions of this new model and open problems. <s> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Particularities of TEL for Recommendation <s> This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations. <s> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Particularities of TEL for Recommendation <s> This article argues that there is a need for Personal Recommender Systems (PRSs) in Learning Networks (LNs) in order to provide learners with advice on the suitable learning activities to follow. LNs target lifelong learners in any learning situation, at all educational levels and in all national contexts. They are community-driven because every member is able to contribute to the learning material. Existing Recommender Systems (RS) and recommendation techniques used for consumer products and other contexts are assessed on their suitability for providing navigational support in an LN. The similarities and differences are translated into specific requirements for learning and specific requirements for recommendation techniques. The article focuses on the use of memory-based recommendation techniques, which calculate recommendations based on the current data set. We propose a combination of memory-based recommendation techniques that appear suitable to realise personalised recommendation on learning activities in the context of e-learning. An initial model for the design of such systems in LNs and a roadmap for their further development are presented. <s> BIB004 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Particularities of TEL for Recommendation <s> GRAPPLE is an EU funded IST FP7 project that brings together a group of researchers into adaptive learning technology and environments and developers of learning management systems (LMSs), in order to offer adaptive learning as a standard feature of future LMSs. This papers presents the overall architecture of GRAPPLE, and explains some of the main challenges in creating a truly general-purpose adaptive learning environment (ALE) that can be used with different LMSs and a distributed architecture of user modeling services. In particular the paper describes the conceptual adaptation model, its translation into adaptation rules, and some technological challenge that results from separating the concerns of adaptation and user modeling. <s> BIB005 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Particularities of TEL for Recommendation <s> This article addresses open questions of the discussions in the first SIRTEL workshop at the EC-TEL conference 2007. It argues why personal recommender systems have to be adjusted to the specific characteristics of learning in Learning Networks. Personal recommender systems strongly depend on the context or domain they operate in, and it is often not possible to take one recommender system with a specific purpose from one context and transfer it to another context or domain. The article describes a number of distinctive differences for personalised recommendation to learners when compared to recommendations for consumers. Similarities and differences for informal and formal learning are discussed and used to define the recommendation goal that recommender systems in informal learning networks have to address. The article further suggests an evaluation approach for recommender systems in Learning Networks. <s> BIB006 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Particularities of TEL for Recommendation <s> In this paper, we outline the importance of discussion fora for e-learning applications. Due to a weak structure or size of the discussion forum, recommendations are required in order to help learners finding relevant information within a forum. We present a generic personalization framework and evaluate the framework based on a recommender architecture for the e-learning focused discussion forum Comtella-D. In the evaluation, we examine different sources of user feedback and the required amount of user interaction to provide recommendations. The outcomes of the evaluation serve as source for a personalization rule, which selects the most appropriate recommendation strategy based on available user input data. We furthermore conclude that collaborative filtering techniques can be utilize successfully in small data sets, like e-learning related discussion fora. <s> BIB007 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Particularities of TEL for Recommendation <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> BIB008
Recommender systems are an extensively studied and well established field of research and application BIB003 . Major search engines like Google and electronic shops like Amazon have incorporated recommendation technology in their services in order to personalize their results. Unfortunately, the algorithms underlying regular recommender systems are not directly transferable to the area of TEL. The TEL area offers some specific characteristics that are not met by today's general purpose recommendation approaches BIB004 . The main difference is, of course, that each learner uses her own tools, methods, paths, collaborations, and processes. Consequently, guidance within the learning process must be personalized to an extreme extent. For example, rather than recommending resources that other users with similar interests have used, the recommendation must also respect the actual learning situation of the learner, including the learning history, environment, timing, and accessible resources. Furthermore, learning activities take place in learning environments that are composed of numerous tools and systems. For example, Learning Management Systems (LMSs) BIB001 as a notion of learning environments provide access to learning resources and collaboration facilities, but do not ensure that teachers or students of a course use them only. Often, learners use additional tools to collaborate or find resources-for instance, in case that the learning material offered in the LMS is not sufficient. Adaptive Learning Environments (ALEs) address this issue by providing support for personalized access to learning material BIB005 . Such systems often track learner progress and provide adaptive page content, for instance to automatically compensate for missing prerequisite knowledge. In addition, Personal Learning Environments (PLEs) BIB008 enable learners to compile the tools they want to address a learning challenge. PLEs also offer the advantage of capturing the learning activity to a greater extent than an LMS can BIB002 . Learning situations become even more complex due to the fact that pedagogical approaches differentiate between formal and informal learning processes. Both have different requirements for the learning environment and, as such, for the recommendation within the environment. Often, it is not possible to draw a clear line between formal and informal learning scenarios. For example, recommender systems need to deal with the tension of recommendations for activities liked by the learner and those required by the teacher  . Consequently, the need for massive amounts of data about the user and her activities within all of her learning environments is necessary to facilitate precise recommendations. This leads to the problem of usage data availability. Many recommendation algorithms rely on massive amounts of usage data from numerous users to make precise recommendations. In TEL, this situation often does not occur. Instead, many learning activities take place with only a few learners participating. This situation occurs in both formal and informal learning settings, e.g., in courses BIB007 or in learning networks BIB006 that assemble and dissolve continuously. At least some tailoring of the respective approach is necessary. In this paper, we outline how such tailoring is possible by taking the context of the learner into account in a much more specific way than applied in today's recommendation approaches. First, we present a brief overview of existing TEL recommender systems that rely on traditional recommendation algorithms. Then, we discuss context definitions and existing research on the incorporation of contextual information in the recommendation process.