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

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.