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From Ideal to Reality: Segmentation, Annotation, and Recommendation, the Vital Trajectory of Intelligent Micro Learning

Journal Article


Abstract


  • The soaring development of Web technologies and mobile devices has blurred time-space

    boundaries of people’s daily activities. Such development together with the life-long learning

    requirement give birth to a new learning style, micro learning. Micro learning aims to

    effectively utilize learners’ fragmented time to carry out personalized learning activities through

    online education resources. The whole workflow of a micro learning system can be separated

    into three processing stages: micro learning material generation, learning materials annotation

    and personalized learning materials delivery. Our micro learning framework is firstly introduced

    in this paper from a higher perspective. Then we will review representative segmentation

    and annotation strategies in the e-learning domain. As the core part of the micro learning

    service, we further investigate several the state-of-the-art recommendation strategies, such as

    soft computing, transfer learning, reinforcement learning, and context-aware techniques. From

    a research contribution perspective, this paper serves as a basis to depict and understand the

    challenges in the data sources and data mining for the research of micro learning.

Authors


  •   Lin, Jiayin (external author)
  •   Sun, Geng (external author)
  •   Cui, Tingru
  •   Shen, Jun
  •   Xu, Dongming (external author)
  •   Beydoun, Ghassan
  •   Yu, Ping
  •   Pritchard, David (external author)
  •   Li, Li (external author)
  •   Chen, Shiping (external author)

Publication Date


  • 2020

Citation


  • Lin, J., Sun, G., Cui, T., Shen, J., Xu, D., Beydoun, G., Yu, P., Pritchard, D., Li, L. & Chen, S. (2020). From Ideal to Reality: Segmentation, Annotation, and Recommendation, the Vital Trajectory of Intelligent Micro Learning. World Wide Web, Online First 1-21.

Number Of Pages


  • 20

Start Page


  • 1

End Page


  • 21

Volume


  • Online First

Place Of Publication


  • United States

Abstract


  • The soaring development of Web technologies and mobile devices has blurred time-space

    boundaries of people’s daily activities. Such development together with the life-long learning

    requirement give birth to a new learning style, micro learning. Micro learning aims to

    effectively utilize learners’ fragmented time to carry out personalized learning activities through

    online education resources. The whole workflow of a micro learning system can be separated

    into three processing stages: micro learning material generation, learning materials annotation

    and personalized learning materials delivery. Our micro learning framework is firstly introduced

    in this paper from a higher perspective. Then we will review representative segmentation

    and annotation strategies in the e-learning domain. As the core part of the micro learning

    service, we further investigate several the state-of-the-art recommendation strategies, such as

    soft computing, transfer learning, reinforcement learning, and context-aware techniques. From

    a research contribution perspective, this paper serves as a basis to depict and understand the

    challenges in the data sources and data mining for the research of micro learning.

Authors


  •   Lin, Jiayin (external author)
  •   Sun, Geng (external author)
  •   Cui, Tingru
  •   Shen, Jun
  •   Xu, Dongming (external author)
  •   Beydoun, Ghassan
  •   Yu, Ping
  •   Pritchard, David (external author)
  •   Li, Li (external author)
  •   Chen, Shiping (external author)

Publication Date


  • 2020

Citation


  • Lin, J., Sun, G., Cui, T., Shen, J., Xu, D., Beydoun, G., Yu, P., Pritchard, D., Li, L. & Chen, S. (2020). From Ideal to Reality: Segmentation, Annotation, and Recommendation, the Vital Trajectory of Intelligent Micro Learning. World Wide Web, Online First 1-21.

Number Of Pages


  • 20

Start Page


  • 1

End Page


  • 21

Volume


  • Online First

Place Of Publication


  • United States