RIS ID
135125
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.
Grant Number
ARC/DP180101051
Publication Details
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.