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From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning

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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.

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Acknowledgements

This research has been carried out with the support of the Australian Research Council Discovery Project, DP180101051, and Natural Science Foundation of China, no. 61877051, and UGPN RCF 2018-2019 project between University of Wollongong and University of Surrey.

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Authors and Affiliations

  1. School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia

    Jiayin Lin, Geng Sun, Tingru Cui, Jun Shen & Ping Yu

  2. Research Lab of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA

    Jun Shen & David Pritchard

  3. UQ Business School, The University of Queensland, Brisbane, QLD, Australia

    Dongming Xu

  4. School of Information, System and Modelling, The University of Technology Sydney, Sydney, NSW, Australia

    Ghassan Beydoun

  5. Faculty of Computer and Information Science, Southwest University, Chongqing, China

    Li Li

  6. Data 61, CSIRO, Sydney, NSW, Australia

    Shiping Chen

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  1. Jiayin Lin
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Correspondence to Jiayin Lin.

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This article belongs to the Topical Collection: Special Issue: Computational Social Science as the Ultimate Web Intelligence

Guest Editors: Xiaohui Tao, Juan D. Velasquez, Jiming Liu, and Ning Zhong

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Lin, J., Sun, G., Cui, T. et al. From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning. World Wide Web 23, 1747–1767 (2020). https://doi.org/10.1007/s11280-019-00730-9

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