Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> TEL Recommender Systems <s> When Linda Silverman and I wrote this paper in 1987, our goal was to offer some insights about teaching and learning based on Dr. Silverman’s expertise in educational psychology and my experience in engineering education that would be helpful to some of my fellow engineering professors. When the paper was published early in 1988, the response was astonishing. Almost immediately, reprint requests flooded in from all over the world. The paper started to be cited in the engineering education literature, then in the general science education literature; it was the first article cited in the premier issue of ERIC’s National Teaching and Learning Forum; and it was the most frequently cited paper in articles published in the Journal of Engineering Education over a 10-year period. A self-scoring web-based instrument called the Index of Learning Styles that assesses preferences on four scales of the learning style model developed in the paper currently gets about 100,000 hits a year and has been translated into half a dozen languages that I know about and probably more that I don’t, even though it has not yet been validated. The 1988 paper is still cited more than any other paper I have written, including more recent papers on learning styles. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> TEL Recommender Systems <s> Universities, experiencing growths in student enrollment and reductions in operating budgets, are faced with the problem of providing adequate help resources for students. Help resources are needed at an institution-wide and also at a course-specific level, due to the limited time of instructors to provide help and answer questions. The Intelligent IntraNet Peer Help Desk provides an integration and application of previously developed ARIES Lab tools for peer help to university teaching. One of its components, CPR, provides a subject-oriented discussion forum and FAQ-list providing students with electronic help. Another component, PHelpS, suggests an appropriate peer to provide human help. In both cases it is peer help, since the help originates from students themselves. The selection of the appropriate help resource (electronic or human) is based on modelling student knowledge and on a conceptual model of the subject material. <s> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> TEL Recommender Systems <s> Digital libraries populated with learning objects are becoming popular tools in the creation of instructional technologies. Many current efforts to create standard metadata structures that facilitate the discovery and instructional use of learning objects recommend a single, authoritative metadata record per version of the learning object. However, as we argue in this paper, a single metadata record — particularly one with fields that emphasize knowledge management and technology, while evading instructional issues — provides information insufficient to support instructional utilization decisions. To put learning objects to instructional use, users must examine the individual objects, forfeiting the supposed benefits of the metadata system. As a solution, we propose a system that includes multi-record, non-authoritative metadata focussed on the surrounding instructional context of learning objects. <s> BIB003 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> TEL Recommender Systems <s> This paper explores two ways to help students locate most relevant resources in educational digital libraries. One method gives a more comprehensive access to educational resources, through multiple pathways of information access, including browsing and information visualization. The second method is to access personalized information through social navigation support. This paper presents the details of the Knowledge Sea II system for comprehensive personalized access to educational resources and also presents the results of a classroom study. The study delivered a convincing argument for the importance of providing multiple information presentations modes, showing that only about 10% of all resource accesses were made through the traditional search interface. We have also collected some solid evidence in favor of the social navigation support. <s> BIB004 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> TEL Recommender Systems <s> Personalized service is important on the Internet, especially in Web-based learning. Generally, most personalized systems consider learner preferences, interests, and browsing behaviors in providing personalized services. However, learner ability usually is neglected as an important factor in implementing personalization mechanisms. Besides, too many hyperlink structures in Web-based learning systems place a large information burden on learners. Consequently, in Web-based learning, disorientation (losing in hyperspace), cognitive overload, lack of an adaptive mechanism, and information overload are the main research issues. This study proposes a personalized e-learning system based on Item Response Theory (PEL-IRT) which considers both course material difficulty and learner ability to provide individual learning paths for learners. The item characteristic function proposed by Rasch with a single difficulty parameter is used to model the course materials. To obtain more precise estimation of learner ability, the maximum likelihood estimation (MLE) is applied to estimate learner ability based on explicit learner feedback. Moreover, to determine an appropriate level of difficulty parameter for the course materials, this study also proposes a collaborative voting approach for adjusting course material difficulty. Experiment results show that applying Item Response Theory (IRT) to Web-based learning can achieve personalized learning and help learners to learn more effectively and efficiently. <s> BIB005 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> TEL Recommender Systems <s> CourseRank is a course planning tool we have developed for Stanford students, and is already in use by most undergraduates. For CourseRank, we have developed a "flexible recommendations" engine for defining recommendation strategies as high level workflows. By selecting a workflow and providing parameters (e.g., a filter condition for biology classes), students can receive personalized recommendations that better suit their needs. In this talk I will give an overview of CourseRank and its recommendation engine. <s> BIB006 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> TEL Recommender Systems <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> BIB007 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> TEL Recommender Systems <s> The paper describes a model for recommendations in learning scenarios which has been designed from empirical findings following usability and accessibility criteria. This model supports course designers in describing recommendations and presents additional information to the user to explain why the recommendation has been provided. A prototype of a recommender system based on this model has been integrated in an open source standard-based learning management system. The main goal of the recommender is to improve the learning efficiency. Examples of recommendations defined with this model are provided. Moreover, a users' experience is reported. <s> BIB008
Recommender systems in TEL are quite diverse. A recent survey of recommender systems in TEL has been presented by Manouselis et al.  . Most systems suggest learning resources  and/or people BIB002 , BIB003 who can help with a learning activity. Course recommenders BIB006 typically provide advice to learners on courses to enroll in. Several social navigation systems  , BIB004 rely on recommendation techniques to suggest resource sequences. While not traditionally considered as recommender systems, Intelligent Tutoring Systems (ITSs)  use information about the learner to suggest personalized hints while she is solving a problem. Recommender systems usually rely on collaborative filtering, content-based filtering, knowledge-based filtering, or hybrid recommendation algorithms. A discussion of the advantages and drawbacks of the various techniques for TEL has been presented in BIB007 . These algorithms use information about users and resources to generate recommendations. Interestingly, most TEL recommender systems rely on profiles of learners or teachers that describe additional information as opposed to interests or preferences only. The knowledge level of the learner is often used to personalize recommendations, such as her knowledge of course concepts BIB005 or past academic grades BIB006 . Learning styles are also considered by some recommender systems in TEL  , BIB008 , often based on the Felder-Silverman BIB001 inventory. Similarly, many systems rely on resource profiles that describe multiple attributes of resources. In addition to general characteristics like keywords, title, and author, many systems use educational metadata that describe for instance the difficulty level of a resource. This difficulty level is then used to recommend resources according to the knowledge level of the learner. In addition, the interactivity level, resource type (e.g., question, definition, or case study) and intended end-user role of the resource are often considered. These attributes are used to suggest different types of resources that are appropriate for the learning activity. Technical characteristics like the format of resources as well as annotation metadata that capture comments from users are also used. CoFIND  for instance relies on metadata that describe what users value in a resource. Examples defined by the authors are "detailed" or "simple to understand." Although multiple attributes are often considered, additional context dimensions (such as the current location, current activity or device) are not incorporated in most of these systems. In this paper, we investigate the way in which such additional contextual information may be used by TEL recommender systems.