A survey of segmentation, annotation, and recommendation techniques in micro learning for next generation of OER

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design, CSCWD 2019, 2019, pp. 152-157
Issue Date:
2019-05-01
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© 2019 IEEE. With the fast development of Internet technologies and mobile devices, space-time boundaries of people's daily activities become blurred. This makes people utilizing their fragmented time become possible. In recent years, micro learning, which aims to make good use of people's fragmented time and deliver micro-format of open education resource to learners, has drawn wider attention. The generation and delivery of micro learning materials are two essential steps for the micro learning service. This paper first discusses the characteristics of three significant stages of a sophisticated micro learning system: segmentation, annotation, and recommendation, for learning materials. Then various state-of-the-art techniques for different processing stages are reviewed in this survey. Different segmentation and annotation strategies based on different information sources (such as content and users' interaction) are demonstrated and analysed. Soft computing, transfer learning, reinforcement learning, and context-aware techniques are also compared and discussed for solving different difficulties in recommending scenarios. We contribute this paper as the first work focusing on the three-phased techniques in micro learning.
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