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    Enhancing Learning Object Recommendations for Teachers Using Adaptive Neighbor Selection

    Access Status
    Fulltext not available
    Authors
    Sergis, S.
    Sampson, Demetrios
    Date
    2015
    Type
    Conference Paper
    
    Metadata
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    Citation
    Sergis, S. and Sampson, D. 2015. Enhancing Learning Object Recommendations for Teachers Using Adaptive Neighbor Selection, in Proceedings of the 15th IEEE International Conference on Advanced Learning Technologies (ICALT 2015), Jul 6-9 2015, pp. 391-393. Hualien, Taiwan: IEEE.
    Source Title
    Proceedings: 15th IEEE International Conference on Advanced Learning Technologies (ICALT 2015)
    Source Conference
    15th IEEE International Conference on Advanced Learning Technologies (ICALT 2015)
    DOI
    10.1109/ICALT.2015.50
    School
    School of Education
    URI
    http://hdl.handle.net/20.500.11937/9234
    Collection
    • Curtin Research Publications
    Abstract

    Recommender Systems (RS) have been implemented in the Technology enhanced Learning (TeL) field for facilitating, among others, Learning Object (LO) selection by teachers to support their daily teaching practice. In particular, memory-based collaborative filtering (CF) approaches have demonstrated promising results for real-life implementations of web-based Learning Object Repositories (LOR). Building on this, the contribution of this paper is an enhancement to existing memory-based CF RS methods, by adaptively selecting the teacher neighbors based on their co-rated LOs and the attribute similarity of the latter to the LO to be recommended. The evaluation results show a significant increase in the predictive accuracy of the adaptive RS approaches compared to their "traditional" benchmarks, signifying the proposed approach's capacity to enhance the accuracy of existing memory-based CF approaches.

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