Towards a Social Trust-Aware Recommender for Teachers

Soude Fazeli*, Hendrik Drachsler, Francis Brouns, Peter Sloep

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    79 Downloads (Pure)


    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.
    Original languageEnglish
    Title of host publicationRecommender Systems for Technology Enhanced Learning
    Subtitle of host publicationResearch Trends and Applications
    EditorsNikos Manouselis, Hendrik Drachsler, Katrien Verbert, Olga C. Santos
    Place of PublicationNew York, NY
    Number of pages18
    ISBN (Electronic)978-1-4939-0530-0
    ISBN (Print)978-1-4939-0529-4
    Publication statusPublished - 2014


    • recommender system
    • social network
    • similarity
    • teacher
    • sparsity
    • learning object
    • collaborative filtering
    • social data
    • trust
    • trust network


    Dive into the research topics of 'Towards a Social Trust-Aware Recommender for Teachers'. Together they form a unique fingerprint.

    Cite this