Towards a social trust-aware recommender for teachers

Abstract

Online communities and networked learning provide teachers with social learning opportunities, allowing them to interact and collaborate with others in order to develop their personal and professional skills. However, with the large number of learning resources produced every day, teachers need to fi nd out what are the most suitable ones for them. In this paper, we introduce recommender systems as a potential solution to this. The setting is the Open Discovery Space (ODS) project. Unfortunately, due to the sparsity of the educational datasets most educational recommender systems cannot make accurate recommendations. To overcome this problem, we propose to enhance a trust-based recommender algorithm with social data obtained from monitoring the activities of teachers within the ODS platform. In this article, we outline the requirements of the ODS recommender system based on experiences reported in related TEL recommender system studies. In addition, we provide empirical evidence from a survey study with stakeholders of the ODS project to support the requirements identifi ed from a literature study. Finally, we present an agenda for further research intended to fi nd out which recommender system should ultimately be deployed in the ODS platform