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

• Published in: World wide web (Bussum) 2020-05, Vol.23 (3), p.1747-1767
• Main Authors: Lin, Jiayin, Sun, Geng, Cui, Tingru, Shen, Jun, Xu, Dongming, Beydoun, Ghassan, Yu, Ping, Pritchard, David, Li, Li, Chen, Shiping
• Format: Article
• Language: English
• Subjects: Annotations; CAI; Computational Social Science as the Ultimate Web Intelligence; Computer assisted instruction; Computer Science; Data mining; Database Management; Distance learning; Educational materials; Electronic devices; Information Systems Applications (incl.Internet); Operating Systems; Segmentation; Soft computing; Workflow
• ISSN: 1386-145X; 1573-1413
• DOI: 10.1007/s11280-019-00730-9

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.