Context-Aware Recommender Systems for Learning: A Survey and Future Challenges

Abstract

Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely diverse and rich environments, the incorporation of contextual information about the user in the recommendation process has attracted major interest. Such contextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs of users, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work in TEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a context framework that identifies relevant context dimensions for TEL applications. Then, we present an analysis of existing TEL recommender systems along these dimensions. Finally, based on our survey results, we outline topics on which further research is needed.

Panorama of Recommender Systems to Support Learning

“…for use on mobile devices in situated learning activities. Ongoing work in this area has been described in [110].…”

Context‐aware movement analytics: implications, taxonomy, and design framework

“…From the methodological perspective, a large body of literature has surveyed the state-of-the-art development of context-aware systems, primarily focusing on system conceptualization, classification framework, architectural stages, challenges during design processes and system integrations, supporting infrastructures, security and privacy issues, etc. [3][4][5][6][7] From an application perspective, Verbert et al 8 specifically presented a survey of context-aware recommendation systems that have been established for technology-enhanced learning. More comprehensively, Liu et al 9 surveyed the existing studies according to the entire spectrum of context-aware mobile recommendation systems and their specifications, seeking to predict and anticipate the needs of users and respond to their behavior.…”

Matching learning material to people with disabilities based on competence and functional capability

“…(1) what competence supports the individual disabled learner, (2) what competence does the disabled learner finish with, and (3) what is the target competence of the disabled learner. This area relates to the individual competence of each learner.…”

“…Moreover, it is often difficult to express a specific requirement using keywords on search engines when the learner does not know totally about the requirement such as suitable format or competence level of the learner [2]. For these reasons, disabled leaners have to spend time and effort to find appropriate content and material to learn on the Internet.…”