6 research outputs found
Towards a Social Trust-Aware Recommender for Teachers
Fazeli, S., Drachsler, H., Brouns, F., & Sloep, P. B. (2014). Towards a Social Trust-aware Recommender for Teachers. In N. Manouselis, H. Drachsler, K. Verbert & O. C. Santos (Eds.), Recommender Systems for Technology Enhanced Learning (pp. 177-194): Springer New York.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 everyday, teachers need to find 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 re-quirements of the ODS recommender system based on experiences reported in related TEL recommender system studies. In addition, we provide empirical ev-idence from a survey study with stakeholders of the ODS project to support the requirements identified from a literature study. Finally, we present an agenda for further research intended to find out which recommender system should ul-timately be deployed in the ODS platform.NELLL, EU 7th framework Open Discovery Spac
Deliverable D.8.4. Social Data Visualization and Navigation Services:3rd Year Update
Within the Open Discovery Space our study (T.8.4) focused on ”Enhanced Social Data Visualization & Navigation Services. This deliverable provides the prototype report regarding the deployment of adapted visualization and navigation services to be integrated in the ODS Social Data Management Layer.Project co-funded by the European Commission within the ICT Policy Support Programme, CIP Competitiveness and innovation framework programme 2007 - 2013. Grant agreement no: 29722
A Survey on Linked Data and the Social Web as facilitators for TEL recommender systems
Personalisation, adaptation and recommendation are central features
of TEL environments. In this context, information retrieval techniques are applied
as part of TEL recommender systems to filter and recommend learning resources
or peer learners according to user preferences and requirements. However,
the suitability and scope of possible recommendations is fundamentally
dependent on the quality and quantity of available data, for instance, metadata
about TEL resources as well as users. On the other hand, throughout the last
years, the Linked Data (LD) movement has succeeded to provide a vast body of
well-interlinked and publicly accessible Web data. This in particular includes
Linked Data of explicit or implicit educational nature. The potential of LD to
facilitate TEL recommender systems research and practice is discussed in this
paper. In particular, an overview of most relevant LD sources and techniques is
provided, together with a discussion of their potential for the TEL domain in
general and TEL recommender systems in particular. Results from highly related
European projects are presented and discussed together with an analysis of
prevailing challenges and preliminary solutions.LinkedU