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Profiling and Supporting Adaptive Micro Learning on Open Education Resources

Conference Paper


Abstract


  • It is found that learners prefer to use micro learning

    mode to conduct learning activities through open educational

    resources (OERs). However, adaptive micro learning is scarcely

    supported by current OER platforms. In this paper we focus on

    profiling an effective micro learning process which is central to

    establish the raw materials and set up rules for the final

    adaptive process. This work consists of two parts. First, we

    conducted an educational data mining and learning analysis

    study to discover the patterns and rules in micro learning

    through OER. Then based on its findings, we profiled features

    of both learners and OERs to reveal the full learning story in

    order to support the decision making process. Incorporating

    educational data mining and learning analysis, an cloud-based

    architecture for Micro Learning as a Service (MLaaS) was

    designed to integrate all necessary procedures together as a

    complete service for delivering micro OERs. The MLaaS also

    provides a platform for resource sharing and exchanging in

    peer-to-peer learning environment. Working principle of a key

    step, namely the computational decision-making of micro OER

    adaptation, was also introduced.

Publication Date


  • 2016

Citation


  • Sun, G., Cui, T., Beydoun, G., Shen, J. & Chen, S. (2016). Profiling and Supporting Adaptive Micro Learning on Open Education Resources. 2016 International Conference on Advanced Cloud and Big Data (pp. 158-163). United States: IEEE.

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6455

Start Page


  • 158

End Page


  • 163

Place Of Publication


  • United States

Abstract


  • It is found that learners prefer to use micro learning

    mode to conduct learning activities through open educational

    resources (OERs). However, adaptive micro learning is scarcely

    supported by current OER platforms. In this paper we focus on

    profiling an effective micro learning process which is central to

    establish the raw materials and set up rules for the final

    adaptive process. This work consists of two parts. First, we

    conducted an educational data mining and learning analysis

    study to discover the patterns and rules in micro learning

    through OER. Then based on its findings, we profiled features

    of both learners and OERs to reveal the full learning story in

    order to support the decision making process. Incorporating

    educational data mining and learning analysis, an cloud-based

    architecture for Micro Learning as a Service (MLaaS) was

    designed to integrate all necessary procedures together as a

    complete service for delivering micro OERs. The MLaaS also

    provides a platform for resource sharing and exchanging in

    peer-to-peer learning environment. Working principle of a key

    step, namely the computational decision-making of micro OER

    adaptation, was also introduced.

Publication Date


  • 2016

Citation


  • Sun, G., Cui, T., Beydoun, G., Shen, J. & Chen, S. (2016). Profiling and Supporting Adaptive Micro Learning on Open Education Resources. 2016 International Conference on Advanced Cloud and Big Data (pp. 158-163). United States: IEEE.

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6455

Start Page


  • 158

End Page


  • 163

Place Of Publication


  • United States