Review of Recommender Systems for Learners in Mobile Social/Collaborative Learning

Social/collaborative learning is a learning procedure that is student-centred and involves a task-based and activity-based approach that collaboratively provides several advantages such as: communication, interpersonal and social co-operation, sharing, caring, openness, creativity, management, practicality, responsibility, involvement and participation. Social and collaborative learning improves pedagogy and are very important aspects of education. Inclusion of social and collaborative learning needs to be considered as a priority in all educational modes. Mobile learning, a new flexible learning landscape is currently being adopted worldwide in both academia and industry. The inclusion of social/collaborative learning in mobile learning is of utmost and vital importance due to its benefits and contributing factors to education/learning efficiency and sustainability. The inclusion of social /collaborative leaning in mobile learning requires the effective management of social activities/data used in learning. Mobile social activities/data involving: non-textual/multimedia (voice/audio and video) and textual that are used by learners and teachers in mobile learning can be extremely large and disorganised with some of the data being educationally irrelevant to the mobile learning process. How educationally relevant mobile social activities/data are realized, structured and managed as well as the filtering of relevant social learning activities/data in mobile learning for learners is a critical issue and needs to be tackled. This paper surveys relevant literature, and proposes recommender systems that can be implemented in mobile social/collaborative learning to solve problems involving the recommendation of relevant social data and learning materials for learners.