A Trust-based Social Recommender for Teachers

Soude Fazeli, Hendrik Drachsler, Francis Brouns, Peter Sloep

    Research output: Chapter in Book/Report/Conference proceedingConference Article in proceedingAcademicpeer-review

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    Online communities and networked learning provide teachers with social learning opportunities to interact and collaborate with others in order to develop their personal and professional skills. In this paper, Learning Networks are presented as an open infrastructure to provide teachers with such learning opportunities. However, with the large number of learning resources produced everyday, teachers need to find out what are the most suitable resources for them. In this paper, recommender systems are introduced as a potential solution to address this issue. Unfortunately, most of the educational recommender systems cannot make accurate recommendations due to the sparsity of the educational datasets. To overcome this problem, we propose a research approach that describes how one may take advantage of the social data which are obtained from monitoring the activities of teachers while they are using our social recommender.
    Original languageEnglish
    Title of host publicationProceedings of the 2nd Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL 2012)
    Subtitle of host publicationOrganised jointly by the 7th European Conference on Technology Enhanced Learning (EC-TEL 2012)
    EditorsNikos Manouselis, Hendrik Drachsler, Katrien Verbert, Olga C. Santos
    Number of pages12
    Publication statusPublished - 12 Sept 2012
    Event2nd Workshop on Recommender Systems for Technology Enhanced Learning - Saarbrücken, Germany
    Duration: 18 Sept 201219 Sept 2012
    Conference number: 2

    Publication series

    SeriesCEUR Workshop Proceedings


    Workshop2nd Workshop on Recommender Systems for Technology Enhanced Learning
    Abbreviated titleRecSysTEL2012


    • Learning Network
    • recommender system
    • teacher
    • social data
    • social networks
    • sparsity
    • trust


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