Abstract
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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.